Dynamic Connectedness and Spillover Effects of CO2 Emissions Among EU Countries: Evidence from the TVP-VAR Connectedness Approach

Dynamic Connectedness and Spillover Effects of CO2 Emissions Among EU Countries: Evidence from the TVP-VAR Connectedness Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamic Connectedness and Spillover Effects of CO2 Emissions Among EU Countries: Evidence from the TVP-VAR Connectedness Approach Çağla Bucak, Abdurrahman Nazif Catik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3805125/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study explores spillover effects of carbon emissions among the 16 EU countries from 1980Q1 to 2023Q3, employing the TVP-VAR connectedness methodology introduced by Antonakakis et al. ( 2020 ). The findings reveal high connectedness, i.e. substantial spillover among the EU countries. Regarding net connectedness measures, the main transmitters of CO 2 emissions are Germany and the UK whereas the main receivers are Greece and Bulgaria. This high connectedness underscores the importance of collaborative efforts among EU countries in formulating policies to mitigate environmental degradation. The findings also indicate a positive correlation between economic activity and pollution, with higher-income countries tending to contribute more to pollution spillover. Our results further suggest that EU member states should endeavor to increase the use of renewable energy sources while phasing out nonrenewable ones, in accordance with the overarching objective of environmental protection, which is to ensure effective environmental protection. Spillover TVP-VAR connectedness CO2 emissions EU Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Climate change poses a substantial peril to the human race, as it possesses the capacity to disturb critical facets of existence such as food provision, water accessibility, environmental conditions, and the steadiness of marine ecosystems (United Nations, 2021 ). Climate change also poses a significant threat to sustainable economic growth through its severe environmental degradation. Hence, it is imperative to conduct a thorough examination of the elements that contribute to climate change in order to identify those that have the potential to disrupt worldwide equilibrium, endanger human life, and cause irreversible damage to the planet (Balsalobre-Lorente et al., 2022 ). One of the negative externalities of continuous economic growth is the release of anthropogenic CO 2 emissions leading to climate change and environmental degradation (Peng et al., 2022 ). Since the start of the 21st century, there has been a consistent uptrend in global fossil CO 2 emissions compared to the three previous decades, primarily attributable to fossil fuel combustion (Crippa et al., 2022 ). Achieving sustained economic growth, which is widely recognized as essential, hinges on the continuous provision of essential inputs like energy. However, a significant majority of countries depend heavily on nonrenewable energy sources to meet their increasing energy needs, which has unquestionably exacerbated environmental challenges (He et al., 2021 ). The rapid trajectory of global climate change is directly associated with the rising trend of global warming and escalating carbon emissions (Doğan et al., 2021 ). There is a growing recent literature analyzing environmental pollution spillovers to assess the impact of recent developments in environmental policies to counter climate change. The growing number of studies on the spillovers may be due to the connection between economic growth and environmental pollution; consequently, environmental concerns in one country may impact the policy decisions of other countries (You & Lv, 2018 ). For instance, closely integrated countries may share a similar development trajectory that results in comparable environmental issues. Hence, it is plausible to hypothesize that the spread of environmental issues from one country to another may significantly impact emission reduction programs in other countries, thereby causing significant environmental degradation concerns (Akram, 2022 ). Given this background, the main objective of the present study is to investigate air pollution spillovers among EU countries. The EU presents an interesting case to study pollution spillovers. First, as the most important example of economic integration, it is one of the largest contributors to global CO 2 emissions (Crippa et al., 2022 ). Second, given this status and to pursue environmental solutions, EU countries aim to become the world’s first carbon-neutral continent by 2050 as part of the Green Deal. Thus, the EU Commission has proposed increasing the mandatory renewable sources target in the EU's energy mix to 40% and achieving a 36–39% reduction in both final and primary energy consumption by 2030 (European Commission, 2020 ). In line with these developments, the energy mix of EU countries has shifted significantly over the past two decades. In 2000, for instance, primary energy consumption by fuel types in the EU stood at 26.79 EJ (exajoules) for oil, 12.92 EJ for natural gas, 11.86 EJ for coal, 8.78 EJ for nuclear energy, 3.79 EJ for hydroelectric power, and 0.65 EJ for renewables. By 2022, consumption of oil, natural gas, coal, nuclear energy, and hydroelectric power had fallen, respectively, to 22.13 EJ, 12.36 EJ, 6.98 EJ, 5.48 EJ, and 2.60, whereas consumption of renewable energy had increased to 8.63 EJ. The increase in the share of renewable energy sources within the EU’s overall energy mix is remarkable, from merely 1 percent to 14.83 percent at present. Conversely, there has been a notable decline in the share contributed by the main three fossil fuels, oil, coal, and natural gas, from approximately 79.6 percent to around 71.28 percent. Nevertheless, despite ongoing efforts towards reducing carbon emissions and promoting sustainable and environmentally friendly sources of energy, it is evident that reaching the EU’s net zero emission target by 2050 remains a considerable challenge, with EU countries currently ranking among the world’s top seven emitters (United Nations, 2023 ). Against this backdrop, the present paper aims to contribute to the literature on pollution spillover in two respects. First, while some studies on EU countries have used spatial econometric methodologies, this study is the first to analyze pollution spillovers among EU countries using the novel TVP-VAR connectedness approach. This methodology provides a more precise and detailed comprehension of pollution spillovers. By identifying both the countries that receive and transmit pollution spillovers, it is possible to gain insights into the specific dynamics of pollution transmission within the EU. This method also permits us to evaluate the magnitude of spillover and its variation across countries and over time. Second, unlike previous studies, we utilize the CO 2 emissions of the countries originally available at a quarterly frequency, allowing us to draw more robust statistical inferences. By addressing these gaps in existing research, the findings can assist EU policymakers and stakeholders in developing effective strategies to reduce pollution and promote sustainable development. The rest of the article is structured as follows. Section two reviews previous research analyzing pollution spillovers among countries, with a specific emphasis on EU countries. Section three describes the methodology of TVP-VAR connectedness. Section four presents the empirical findings using various connectedness measures. Section five summarizes the main findings and suggests some policy implications. 2. Literature review The historical framework indicates that environmental problems have been recognized for over five decades, with various conferences and summits being held, and agreements ratified to address the adverse impacts of the climate crisis. To combat environmental pollution and climate change and reverse the decline in biodiversity rates, countries have committed to achieving carbon neutrality by 2050, according to their treaty endorsements. Given these developments, it is evident that any examination of environmental issues within the empirical economics literature requires comprehensive and more complex methodologies. However, conventional econometric methodologies, which do not allow for interdependencies among the countries or regions, may fail to account for the analysis of pollution spillover effects. Taking into consideration these factors, several scholars have employed spatial econometric models to analyze pollution spillovers between neighboring countries. Researchers including Zhang et al. ( 2017 ), You and Lv ( 2018 ), Zhang et al. ( 2018 ), Li et al. ( 2019 ), Abdo et al. ( 2020 ), Gu et al. ( 2020 ), Li and Li ( 2020 ), Murshed et al. ( 2020 ), Li and Wang ( 2022 ), Pea-Assounga and Wu ( 2022 ), Wu et al. ( 2022 ), Jeetoo and Chinyanga ( 2023 ), Qunfang and Huang ( 2023 ), and Tawfeeq ( 2023 ) have found positive and significant CO 2 emission spillovers between neighboring countries. Conversely, Al-Silefanee et al. ( 2022 ) and Karimi et al. ( 2022 ) did not find significant spillover while Wen et al. ( 2020 ) and Lin et al. ( 2022 ) reported negative spillover effects of CO 2 emissions. A few studies have analyzed spillover effects of environmental degradation among EU countries using spatial econometric techniques. For instance, Ren et al. ( 2020 ) reported a significant positive spatial spillover of CO 2 emissions from 26 adjacent EU countries to the host country. Focusing on 21 EU countries Radmehr et al. ( 2021 ) found that a 1% increase in CO 2 emissions in neighboring countries leads to a 0.06% rise in CO 2 emissions within the host country. Similarly, for 28 EU countries, Shahnazi and Shabani ( 2021 ) found that increasing CO 2 emissions in a country’s neighboring region results in an increase in the country’s own CO 2 emissions. In short, these studies demonstrate that environmental degradation spills over across EU countries. Along with the spatial econometric techniques, spillover among the variables has recently been analyzed using a spillover index developed by Diebold and Yilmaz ( 2009 ). They calculated time-varying spillovers using forecast error decompositions obtained from the rolling estimation of VAR models. Diebold and Yilmaz ( 2012 ) then extended the DY spillover index by calculating directional spillovers based on generalized forecast error variances independent of the ordering of the variables. Furthermore, Diebold and Yilmaz ( 2014 ) introduced a connectedness approach based on forecast error variation in different locations due to shocks occurring anywhere and provided ways for assessing connectedness derived from their prior methodologies. These techniques can be used to measure both own effect of the variables and those of others by distinguishing between net shock transmitters and net shock receivers (Antonakakis et al., 2020 ). Antonakakis et al. ( 2020 ) measured connectedness based on the TVP-VAR model instead of rolling window VAR. In this methodology, there is no requirement to set a window size for estimating the VAR model; rather, connectedness measures are calculated from time-varying decomposition without any loss of observations. In our review of the literature, only two studies, i.e., Akram ( 2022 ) and Shirazi and Šimurina ( 2022 ), investigating pollution spillovers based on the connectedness approach, were identified. While our study relies on quarterly data for EU countries, Akram ( 2022 ) employs annual data and analyzes spillover and connectedness of agricultural GHG emissions across continents. While Akram ( 2022 ) exclusively focuses on GHG emissions, Shirazi and Šimurina ( 2022 ) employ CO 2 emissions categorized by sector and source from energy consumption in the USA. These studies examined the connectedness of environmental degradation across continents and the USA, respectively. Our study seeks to fill several research gaps in the literature. Firstly, in contrast to studies employing lower-frequency annual data, our study utilizes CO 2 emissions originally available at a quarterly frequency. This allows us to capture the time-varying nature of environmental degradation, providing a more accurate assessment of spillovers among countries. Secondly, our study targets EU countries. Focusing on the EU countries enables us to derive insights into the effectiveness of international agreements and collaborations, such as the Paris Climate Agreement and the Green New Deal. Our study contributes to the formulation and discussion of necessary policies within the framework of international cooperation. Thirdly, to the best of our knowledge, no studies have investigated CO 2 emission spillovers among EU countries using Antonakakis et al.’s ( 2020 ) TVP-VAR connectedness approach, which offers greater robustness and reliability compared to the methodologies employed in previous studies. 3. Methodology This study used the TVP-VAR connectedness approach developed by Antonakakis et al. ( 2020 ) based on Diebold and Yilmaz’s ( 2014 ) connectedness approach. The structure of the estimated TVP-VAR model, allowing for the variance-covariance matrix to change over time, is defined by the following three sets of equations: \({y}_{t}={A}_{t}{z}_{t-1}+{\epsilon }_{t} {\epsilon }_{t}\left|{{\Omega }}_{t-1}\sim N\right.\) (0, \({ {\Sigma }}_{t})\) (1) vec ( \({A}_{t})=\) vec ( \({A}_{t-1})+{{\xi }}_{t} {{\xi }}_{t}\left|{{\Omega }}_{t-1}\sim N \right.\) (0, \({ {\Xi }}_{t})\) (2) with $${z}_{t-1}= \left(\begin{array}{c}{y}_{t-1}\\ {y}_{t-2}\\ .\\ .\\ .\\ {y}_{t-p}\end{array}\right) {A}_{t}^{{\prime }}=\left(\begin{array}{c}{A}_{1t}\\ {A}_{2t}\\ .\\ .\\ .\\ {A}_{pt}\end{array}\right)$$ 3 Here, \({{\Omega }}_{t-1}\) denotes the available information until t-1. \({y}_{t}\) , \({z}_{t-1}\) and \({\epsilon }_{t}\) represents m x 1, mp x 1 and m x 1 vectors, including the current and lagged values of CO 2 emissions respectively. \({A}_{t}\) and \({A}_{t}^{{\prime }}\) show the m x mp and m x m dimensional matrices, respectively. \({{\xi }}_{t}\) is the m 2 p x 1 dimensional vector. Time-varying variance-covariance matrices are denoted by \({{\Sigma }}_{t}\) and \({{\Xi }}_{t}\) are with m x m and m 2 p x m 2 p dimensions respectively. Finally, vec ( \({A}_{t})\) represents the vectorized form of \({A}_{t}\) with a m 2 p x 1 dimension. In the TVP-VAR connectedness approach, connectedness measures are computed from generalized impulse response functions (GIRF) and generalized forecast error variance decompositions (GFEVD) (Diebold & Yilmaz, 2014 ). In order to achieve this, the TVP-VAR model is converted into the vector moving average (VMA) form. The representation, based on the Wold theorem, is given by: $${y}_{t}={J}^{{\prime }}({M}_{t}\left({z}_{t-2}+{\eta }_{t-1}\right)+{n}_{t})$$ 4 $$={J}^{{\prime }}({M}_{t}\left({M}_{t}\left({z}_{t-3}+{\eta }_{t-2}\right)+{n}_{t-1}\right)+{n}_{t})$$ 5 $$⋮$$ 6 $$={J}^{{\prime }}({M}_{t}^{k-1}{z}_{t-k-1}+\sum _{j=0}^{k}{M}_{t}^{j}{\eta }_{t-j})$$ 7 with $${M}_{t}=\left(\begin{array}{cc}{A}_{t}& .\\ {I}_{m(p-1)}& {0}_{m\left(p-1\right) x m}\end{array}\right) {n}_{t}= \left(\begin{array}{c}{ϵ}_{t}\\ 0\\ .\\ .\\ .\\ 0\end{array}\right)= J{ϵ}_{t} J=\left(\begin{array}{c}I\\ 0\\ .\\ .\\ .\\ 0\end{array}\right)$$ 8 Here, \({M}_{t}\) represents the mp x pm dimensional matrix whereas \(J\) denotes the mp x m dimensional matrix and \({n}_{t}\) stands for the mp x 1 dimensional vector. Taking the limit as k approaches ∞ gives the following: $${y}_{t}=\underset{k\to {\infty }}{\text{lim}}{J}^{{\prime }}({M}_{t}^{k-1}{z}_{t-k-1}+\sum _{j=0}^{k}{M}_{t}^{j}{\eta }_{t-j})=\sum _{j=0}^{{\infty }}{{J}^{{\prime }}M}_{t}^{j}{\eta }_{t-j}$$ 9 $${y}_{t}=\sum _{j=0}^{{\infty }}{{J}^{{\prime }}M}_{t}^{j}J{ϵ}_{t-j} {B}_{jt}= {{J}^{{\prime }}M}_{t}^{j}J, j=\text{0,1},\dots$$ 10 $${y}_{t}=\sum _{j=0}^{{\infty }}{B}_{jt}{ϵ}_{t-j}$$ 11 Here \({B}_{jt}\) indicates an m x m dimensional matrix. The GIRFs ( \({\psi }_{ij,t}\left(H\right))\) show the responses of all variables j following a shock in variable i . Here, the differences between an H-step-ahead forecast are calculated with and without variable i being shocked. This can be computed in the following manner: $${GIRF}_{t}\left(H,{\delta }_{j,t},{{\Omega }}_{t-1}\right)=E\left({y}_{t+H}|{e}_{j}={\delta }_{j,t},{{\Omega }}_{t-1}\right)-E({y}_{t+J}│{{\Omega }}_{t-1})$$ 12 $${\psi }_{j,t}\left(H\right)=\frac{{B}_{H,t}{\sum }_{t}{e}_{j}}{\sqrt{{\sum }_{jj,t}}}\frac{{\delta }_{j,t}}{\sqrt{{\sum }_{jj,t}}} {\delta }_{j,t}= \sqrt{{\sum }_{jj,t}}$$ 13 $${\psi }_{j,t}\left(H\right)={\sum }_{jj,t}^{- \frac{1}{2}}{B}_{H,t}{\sum }_{t}{e}_{j}$$ 14 Here, \({e}_{j}\) is an m x 1 selection vector with unity in the jth rank, and zero otherwise. GFEVD( \({\stackrel{\sim}{\varphi }}_{ij,t}\left(H\right))\) is pairwise directional connectedness from j to i . It represents the effect variable j has on variable i . It is measured as: $${\stackrel{\sim}{\varphi }}_{ij,t}\left(H\right)=\frac{\sum _{t=1}^{H-1}{\psi }_{ij,t}^{2}}{\sum _{j=1}^{m}\sum _{t=1}^{H-1}{\psi }_{ij,t}^{2}}$$ 15 where \(\sum _{j=1}^{m}{\stackrel{\sim}{\varphi }}_{ij,t}\left(H\right)\) = 1 and \(\sum _{i,j=1}^{m}{\stackrel{\sim}{\varphi }}_{ij,t}\left(H\right)\) = m . In Eq. 15 , the denominator indicates the cumulative impact of all the shocks, whereas the numerator shows the cumulative impact of a shock of in variable i . Using Eq. 15 , the total connectedness index is constructed as follows: $${C}_{t}\left(H\right)=\frac{\sum _{i, j=1, i\ne j }^{m}{\stackrel{\sim}{\varnothing }}_{ij,t}\left(H\right)}{\sum _{i, j=1}^{m}{\stackrel{\sim}{\varnothing }}_{ij,t}\left(H\right)}*100=\frac{\sum _{i, j=1, i\ne j }^{m}{\stackrel{\sim}{\varnothing }}_{ij,t}\left(H\right)}{m}*100.$$ 16 This connectedness index shows how a shock that occurred in one variable spills over to other variables. If variable i transmits its shock to all other variables j , it is labelled “total directional connectedness to others ” and calculated as follows: $${C}_{i\to \text{j},\text{t}}\left(H\right)=\frac{\sum _{j=1, i\ne j }^{m}{\stackrel{\sim}{\varnothing }}_{ij,t}\left(H\right)}{\sum _{j=1}^{m}{\stackrel{\sim}{\varnothing }}_{ij,t}\left(H\right)}*100.$$ 17 When variable i receives shocks from all variables j , it is labelled “total directional connectedness from others” and calculated as follows: $${C}_{i\leftarrow \text{j},\text{t}}\left(H\right)=\frac{\sum _{j=1, i\ne j }^{m}{\stackrel{\sim}{\varnothing }}_{ij,t}\left(H\right)}{\sum _{i=1}^{m}{\stackrel{\sim}{\varnothing }}_{ij,t}\left(H\right)}*100.$$ 18 “Net total directional connectedness,” which is the difference between total directional connectedness to others and total directional connectedness from others, illustrates the impact of variable i on the network. It is written as follows: $${C}_{i,t}={C}_{i\to \text{j},\text{t}}\left(H\right)-{C}_{i\leftarrow \text{j},\text{t}}\left(H\right)$$ 19 If \({C}_{i,t}\) is positive, variable i affects the network more than being affected itself. Conversely, if it is negative, variable i is influenced by the network. Lastly, net total directional connectedness can be broken down to measure bidirectional relationships by calculating “net pairwise directional connectedness.” $${NPDC}_{ij }\left(H\right)={\stackrel{\sim}{\varnothing }}_{jit}\left(H\right)-{\stackrel{\sim}{\varnothing }}_{jit}\left(H\right))*100.$$ 20 If \({NPDC}_{ij }\left(H\right)\) is higher than zero, it indicates that variable i dominates variable j . When it is lower than zero, it indicates that variable j dominates variable i . 4. Dataset This study aimed to estimate dynamic connectedness and spillover of CO 2 emissions among 16 EU countries. To do so, quarterly CO 2 emissions data from 1980Q1 to 2023Q3 were derived from the Refinitiv Eikon Datastream (2023) database. The countries and the period were selected based on data availability. The 16 sampled EU countries were Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Romania, Spain, Sweden, and the United Kingdom.[1] CO 2 emissions were converted to their natural logarithm before the analysis. Descriptive statistics for the CO 2 emissions are presented in Fig. 1 and Table 1 . Figure 1 shows that CO 2 emissions have decreased synchronously in the 16 countries. Germany is the largest CO 2 emitter, followed by the United Kingdom, Italy, and France. Notably, the COVID-19 outbreak resulted in a significant decrease in emissions in 2020, which can be attributed to various restrictive measures, such as stay-at-home directives, lockdown protocols, travel limitations, border closures impacting trade, and a subsequent decrease in production. However, the EU’s CO 2 emissions surged after the COVID-19 pandemic ended due to a return to pre-pandemic conditions. Table 1 Descriptive Statistics Mean Variance Skewness Ex.Kurtosis JB Q(10) Q 2 (10) Austria 1.412 0.013 -0.044 -0.861*** 5.459* 799.179*** 799.014*** Belgium 1.94 0.012 -0.734*** 0.326 16.478*** 618.318*** 626.894*** Bulgaria 1.268 0.081 0.623*** -0.936*** 17.705*** 832.223*** 842.606*** Denmark 1.162 0.082 -0.975*** -0.169 27.909*** 785.338*** 767.767*** Finland 1.25 0.030 -0.301* -0.484 4.353 702.062*** 686.967*** France 3.144 0.013 -0.666*** 1.293*** 25.128*** 630.293*** 627.832*** Germany 4.021 0.023 -0.321* -0.505 4.858* 800.519*** 803.958*** Greece 1.544 0.056 -0.287 -1.045*** 10.366*** 819.930*** 839.063*** Ireland 0.84 0.031 -0.097 -0.944*** 6.774** 865.287*** 870.284*** Italy 3.224 0.018 -0.445** -0.259 6.255** 798.034*** 807.339*** Netherlands 2.332 0.013 -1.620*** 3.018*** 142.935*** 615.479*** 635.284*** Portugal 1.048 0.082 -0.682*** -0.698*** 17.130*** 818.652*** 830.337*** Romania 1.958 0.137 0.439** -1.230*** 16.642*** 875.589*** 878.979*** Spain 2.767 0.037 0.23 -0.885*** 7.248** 839.118*** 843.590*** Sweden 1.19 0.047 -0.540*** -0.594** 11.062*** 769.782*** 753.306*** UK 3.475 0.042 -1.474*** 1.108** 72.318*** 777.582*** 787.142*** Note: *, **, and *** denote statistical significance at 10%, 5%, and 1% level, respectively. JB stands for the Jarque and Bera ( 1980 ) normality test. Skewness and kurtosis were calculated using the methods of D’Agostino ( 1970 ) and the Anscombe and Glynn ( 1983 ) statistics. Q(10) and Q 2 (10) represent the weighted Ljung-Box statistic for assessing serial correlation in CO 2 emissions and its squared value, as proposed by Fisher and Gallagher ( 2012 ). Table 2 Unit Root Tests Level ADF PP Variables Intercept Trend and Intercept Intercept Trend and Intercept Austria -1.5499 -0.5303 -1.4443 -1.2265 Belgium 0.8597 -0.9348 -1.1173 -2.1068 Bulgaria -1.1026 -1.9047 -1.2169 -2.0842 Denmark 0.0144 -1.6159 -0.0997 -1.5721 Finland -1.1077 -1.4116 -1.269 -1.5291 France -0.9865 -2.4001 -1.7018 -2.3861 Germany 0.0621 -3.0539 0.0202 -2.4771 Greece -1.4559 -0.4062 -0.9935 -0.1061 Ireland -1.4956 -0.7192 -1.5139 -0.725 Italy -0.2246 -0.807 -0.0859 -0.5593 Netherlands -1.1373 0.6242 1.2869 3.4565 Portugal -2.0617 -1.1956 -2.0585 -0.957 Romania -0.9523 -2.4498 -0.8468 -2.1475 Spain -1.3888 -0.7252 -1.4254 -0.7252 Sweden 0.7234 -1.7602 -1.0426 -2.5889 UK 1.0666 -0.724 1.4006 -0.741 First-difference ADF PP Intercept Trend and Intercept Intercept Trend and Intercept ΔAustria -5.8448*** -8.7891*** -8.4339*** -8.458*** ΔBelgium -2.9789** -3.3433* -12.6952** -13.2774* ΔBulgaria -5.8225*** -5.82*** -4.6272*** -4.5784*** ΔDenmark -8.891*** -6.7973*** -4.3146*** -4.2862*** ΔFinland -8.3968*** -4.8409*** -4.9515*** -4.8314*** ΔFrance -8.569*** -8.5431*** -13.9414*** -13.898*** ΔGermany -4.382*** -4.4314*** -11.4911*** -11.6035*** ΔGreece -1.8133 -3.6355** -5.715 -4.5587** ΔIreland -13.6888*** -13.8797*** -13.6951*** -13.8596*** ΔItaly -13.866*** -6.5483*** -13.8608*** -14.379*** ΔNetherlands -1.6934 -3.2865* -12.2917 -12.6534* ΔPortugal -14.468*** -14.7551*** -14.4436*** -14.9856*** ΔRomania -6.3156*** -6.3011*** -6.417*** -6.4001*** ΔSpain -13.2618*** -13.3903*** -13.2618*** -13.3903*** ΔSweden -5.2247*** -5.444*** -11.166*** -11.1159*** ΔUK -16.8056*** -17.0111*** -17.6516*** -21.0604*** Note: The lag length for the ADF test was selected based on the Schwarz information criterion (SIC). The PP test was estimated on the basis of the Bartlett–Kernel test, using the Newey–West bandwidth. The null hypothesis is that the series are nonstationary. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively. The descriptive statistics in Table 1 reveal that CO 2 emissions vary significantly among the 16 countries. For example, Belgium has the lowest variance, while Romania has the highest. Regarding mean values, Germany, the UK, Italy, and France have the highest mean CO 2 emissions levels while Denmark, Portugal, and Ireland have the lowest. The CO 2 emissions of Belgium, Denmark, Finland, France, Germany, Italy, the Netherlands, Portugal, Sweden, and the UK are significantly negatively skewed whereas those of Bulgaria and Romania are significantly positively skewed. Austria, Bulgaria, Greece, Ireland, Portugal, Romania, Spain, and Sweden have platykurtic distributions whereas France, the Netherlands, and the UK have notably leptokurtic distributions. Based on the Jarque-Bera test, only Finland’s emissions data follow a normal distribution. The results of the Q(10) and Q 2 (10) tests indicate autocorrelation among emissions while the unit root test results shown in Table 2 indicate that all the series exhibit stationarity at their first difference. Therefore, all the series are integrated of order one (I(1)). The evidence of the non-stationarity of all variables suggests that CO 2 emissions should be included in their first difference form in the TVP-VAR estimation. 5. Empirical Findings After defining the time series properties of the CO 2 emissions, the TVP-VAR model defined by equations (1), (2), and (3) was estimated, and the connectedness measures defined in the methodology section were calculated to analyze pollution spillovers among the 16 EU countries. Figure 2 presents the results from the dynamic total connectedness measure defined by Eq. ( 16 ). As is evident from the black-shaded area, the countries are interconnected, with the extent of connectedness ranging between 68% and 92%. Hence, it can be inferred that pollution spillover among these EU countries is substantial and varies considerably over time. The connectedness fell in 2009 due to the financial crisis, which led to bankruptcies and reduced production. This in turn reduced CO 2 emissions because higher production levels are typically associated with increased pollution. This finding is supported by Peters et al. ( 2012 ), who concluded that the global financial crisis only affected emissions for a short time due to the decline in production-based emissions and a significant decrease in international trade, stemming from the declining trend of consumption-based emissions. In particular, Declercq et al. ( 2011 ) highlighted that the economic downturn in 2008 led to a substantial decrease in economic activity. Industrial activity, as well as electricity and fuel demand, fell significantly. Conversely, emissions rose as countries emerged from the crisis due to increasing trade, production, and growth levels, and consequently greater connectedness. Connectedness also decreased in 2020 due to the COVID-19 outbreak, leading to a fall in CO 2 emissions whereas it increased after COVID-19 restrictions were lifted. In alignment with our findings, Quéré et al. ( 2021 ) argued that the temporary measures adopted by countries have only a minimal influence on the prevailing fuel-based structure across all countries. Consequently, the fundamental causes behind emissions resurfaced. Similarly, Nguyen et al. ( 2021 ) argued that substantial falls in output, transportation, and energy demand due to the limited movement of individuals during the pandemic led to significant drops in emissions of greenhouse gases, such as CO 2 . These arguments are also valid for EU countries (Jawadi et al., 2023 ). Figure 2 shows that connectedness after the COVID-19 outbreak seems to have become stronger, with countries’ emissions spilling over and affect themselves. Table 3 summarizes the connectedness of each country to identify the emissions transmitters and receivers among the 16 EU countries. The TCI value is 75.45, indicating a strong connectedness relationship between these countries. Particularly notable co-movements are observed between Belgium and Germany, Bulgaria and Romania, Denmark and Finland, Ireland and the UK, Italy, and Spain, and Portugal and Spain. Interestingly, these co-movements may have occurred because these countries are also close geographically. Regarding their contributions to others, the UK, Germany, Italy, and France exhibit the highest spillover effects on other countries, with values of 96.64, 95.02, 93.26, 91.39, respectively. Conversely, the UK and France are significantly influenced by other countries, with values of 81.49 and 80.93, respectively. In terms of net connectedness measures, Germany and the UK emerge as the main CO 2 transmitters within the network, with net values of 15.26 and 15.15, respectively. Conversely, Greece and Bulgaria are the main receivers, with net values of -30.34 and − 14.85, respectively. Interestingly, the GDP per capita values of the main transmitter countries are higher than those of the receiver countries.[2] That is, higher income countries tend to generate and transmit more pollution. Finally, Austria, Belgium, France, Ireland, Italy, Portugal, and Spain are identified as transmitter countries within this network whereas Denmark, Finland, the Netherlands, Romania, and Sweden are identified as receiver countries. Table 3 Dynamic Connectedness Austria Belgium Bulgaria Denmark Finland France Germany Greece Ireland Italy Netherlands Portugal Romania Spain Sweden UK From Austria 22.53 5.91 3.00 6.83 5.17 6.67 8.86 1.61 5.21 6.59 6.06 3.74 3.17 3.52 4.59 6.55 77.47 Belgium 7.11 21.86 1.92 3.43 4.05 10.14 11.70 2.25 5.14 5.37 7.33 2.32 1.50 2.20 6.75 6.93 78.14 Bulgaria 4.29 2.61 32.42 3.06 2.82 3.43 4.20 5.02 2.83 5.81 2.08 3.90 17.37 4.24 2.75 3.17 67.58 Denmark 6.41 4.24 2.37 27.20 17.81 4.08 6.29 1.51 2.18 2.09 4.49 5.63 1.74 3.56 6.44 3.97 72.80 Finland 6.05 5.85 2.24 16.13 28.60 2.75 7.00 2.47 3.29 2.92 3.78 5.14 1.91 2.16 5.85 3.86 71.40 France 7.06 8.87 1.56 3.12 2.02 19.07 6.63 1.75 6.19 9.12 7.97 5.28 1.52 6.07 4.31 9.47 80.93 Germany 8.10 9.81 2.79 6.10 5.46 6.28 20.24 2.44 3.81 4.57 7.26 3.57 3.88 3.14 6.28 6.27 79.76 Greece 2.70 3.59 6.16 2.23 3.40 3.83 3.81 28.10 7.08 7.66 2.53 6.14 5.51 8.64 3.42 5.20 71.90 Ireland 6.17 4.75 1.57 1.80 2.65 7.05 5.08 4.09 21.35 7.64 5.18 5.76 1.95 8.54 3.98 12.43 78.65 Italy 6.31 4.28 3.69 1.25 2.21 8.50 4.64 3.72 7.12 20.94 4.78 7.64 3.27 11.05 2.82 7.78 79.06 Netherlands 7.29 8.01 1.44 4.88 3.27 8.55 9.07 1.87 5.37 6.12 21.35 2.24 1.77 3.46 6.51 8.79 78.65 Portugal 2.78 2.77 1.96 4.83 5.76 6.95 3.06 2.74 6.27 8.99 2.25 27.36 2.06 15.75 1.30 5.16 72.64 Romania 3.76 2.60 17.80 2.19 2.52 3.08 5.60 4.01 3.11 4.72 2.41 3.45 35.06 3.54 2.33 3.82 64.94 Spain 3.90 2.14 2.52 5.49 4.75 6.40 3.28 3.81 7.73 10.72 3.26 13.32 2.15 20.87 2.77 6.88 79.13 Sweden 6.34 8.15 2.00 8.04 5.75 4.98 8.55 1.90 4.56 3.37 7.11 1.58 1.95 2.07 27.30 6.35 72.70 UK 6.54 5.91 1.70 3.00 2.21 8.69 7.24 2.35 10.75 7.58 7.42 4.27 2.55 6.22 5.05 18.51 81.49 To 84.82 79.49 52.73 72.37 69.85 91.39 95.02 41.56 80.64 93.26 73.90 73.99 52.31 84.15 65.15 96.64 1207.2 Net 7.35 1.35 -14.85 -0.43 -1.55 10.46 15.26 -30.34 1.99 14.20 -4.75 1.35 -12.63 5.02 -7.55 15.15 TCI:75.45 Note: TCI stands for Total Connectedness Index. A positive net value indicates that a country is a net transmitter of carbon emissions while a negative net value indicates that it is a net receiver. Figures 3 , 4 , and 5 display how connectedness measures varied over the analysis period. The results show that pollution spillovers were time-varying in that all 16 countries have been both transmitters and receivers at some point. That is, carbon emissions within the EU certainly spill over, affecting each country’s own emissions and environmental policies. These spillover findings are similar to those of Akram ( 2022 ) and Shirazi and Šimurina ( 2022 ), who applied the DY methodology. For EU countries, the existence of spillover is supported by the findings of Ren et al. ( 2020 ), Radmehr et al. ( 2021 ) and Shahnazi and Shabani ( 2021 ), who used spatial econometric models. of the presence of spillover and connectedness indicates that the EU countries must collaborate when designing policies aimed at mitigating environmental degradation. These policy implications will be discussed in the conclusion section. 6. Conclusion In contrast to previous studies using spatial techniques and methods based on the DY methodology, to the best of our knowledge, our study is the first to apply the methodology introduced by Antonakakis et al. ( 2020 ) to EU countries using quarterly CO 2 emissions data. The EU countries are of great significance as they are at the forefront of the global climate and energy crises, with the capacity to influence environmental issues globally. Accordingly, we analyzed the spillover effects and dynamic connectedness measures of 16 EU countries. Our findings indicate that the UK, Germany, Italy, and France have the highest spillover effects on other countries. Given that these countries also have the highest CO 2 emissions, this finding is important and interesting. Regarding net connectedness measures, Germany and the UK are the primary CO 2 transmitters whereas Greece and Bulgaria are the main receivers. These findings align with previous observations that countries with higher incomes tend to contribute more to pollution spillovers whereas countries with lower incomes tend to suffer more from spillovers. The dynamic connectedness between pairs of countries also reveals that the closer countries are geographically, the more interconnected they tend to be. These findings have four significant policy consequences. First, they indicate that environmental problems are not confined to one country and cannot be solved in isolation due to spillover effects among countries. This conclusion is also supported by United Nations, which maintains that addressing climate change requires an unprecedented level of global cooperation. Therefore, countries need to question their economic models, invent new industries, and value nature far beyond money to force rich nations to recognize their moral responsibility to the rest of the world (United Nations, 2021 ). Due to the existence of spillover effects, the EU countries should collaborate in developing carbon mitigation technologies, share the associated costs, and provide incentives to firms or member countries that develop patents to address environmental issues while also raising the expenses linked to higher emissions. Second, as higher-income countries are the primary pollution transmitters, there is a trade-off between economic growth and environmental preservation. However, sustainable development necessitates both economic progress and environmental protection. Therefore, it is critical to decouple growth from environmental degradation at this juncture, as emphasized in the 8th Sustainable Development Goal (UNDP, 2023 ). To ensure environmental protection, the EU countries must strive to achieve the goal of increasing the share of renewables and completely phasing out nonrenewable sources as soon as possible. Third, as connectedness is stronger if countries are closer geographically, these countries must carefully consider the environmental consequences of their actions and policies. In particular, when formulating policies, they should be aware that they affect not only to themselves but also neighboring countries. Fourth, even though various conferences, summits, and ratified agreements emphasize the key role of international cooperation, such as the EU’s inclusion in the Paris Agreement and the Green New Deal, more than mere highlighting is required. Concrete actions are needed, commitments must be taken seriously, and targets must be achieved promptly. Awareness of these points will ensure a sustainable world with a clean environment and economic development. In the present study, only CO 2 emissions were used as a proxy for environmental pollution due to the limited availability of high-frequency data for other indicators. However, carbon emissions only reflect air pollution and ignore soil and water pollution. To overcome this drawback, future studies could incorporate other indicators, such as the ecological footprint. Finally, future studies could conduct a similar analysis for the top large emitting countries. Declarations [Competing Interests: none, no conflict of interest] [Acknowledgements: none, no funds were received] [Funding: none, not applicable] [Ethical approval: This article does not contain any studies with human participants performed by any of the authors] Consent for publication: Our study does not contain individual person’s data. Consent to Participate: No human or animal subjects were used in our study, and no questionnaire was conducted. 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Journal of Cleaner Production , 161 , 1085–1093. https://doi.org/10.1016/j.jclepro.2017.05.071 Footnotes Even though the UK left the EU in 2020, we included it because, between 1980 and 2020, as well as after 2020, its emissions may have influenced the environmental problems of the EU countries. We obtained GDP per capita (constant 2010 US $ ) data from Refinitiv Eikon DataStream (2023), with the latest data being applicable to 2022. According to the 2022 data, Greece and Bulgaria have income values of 20,167 and 9,502, respectively. In contrast, the UK’s and Germany’s income values are 47,232 and 43,032, respectively. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3805125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272275159,"identity":"c396849c-d34e-44e0-924b-7a2e7fb9cbc9","order_by":0,"name":"Çağla Bucak","email":"","orcid":"","institution":"Ege University: Ege Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Çağla","middleName":"","lastName":"Bucak","suffix":""},{"id":272275160,"identity":"10f353a9-399e-4951-8993-6fc471340fe6","order_by":1,"name":"Abdurrahman Nazif Catik","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYNACAzkwdbChAsKXIEKLMZBgBmo5A9diQEgPRAtjYxsRWvgbeA9+/FFgwMDffv7gwZnzbOz6GZgP3uZh+JOPS4vEAb5kCQkDAwaJM8kMBzduS0ue2cCWbM3DYGDZgEvPAR4DoI4/QIcAtTzcdjjZ4ACPmTRQC06XyR/gMf6RAJQ34H8M1DLnf7L9Af5veLWAzJQ4ANIiAXJYwwE7AwYeNrxaDA/zmFk2GBjwSNx4bHBwxrHkBInDbMaWcwyMcWqRO95jfPPHHwM5/v7Exx97auzs+dubH954UyGHO5SZIRQPjJ/YABYhGJNIwJ4EtaNgFIyCUTBCAACH8k0r23+GZQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9247-5668","institution":"Ege Üniversitesi","correspondingAuthor":true,"prefix":"","firstName":"Abdurrahman","middleName":"Nazif","lastName":"Catik","suffix":""}],"badges":[],"createdAt":"2023-12-25 16:06:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3805125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3805125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51058433,"identity":"4ff17bd1-b571-48dd-ac0a-cd49a7faf6af","added_by":"auto","created_at":"2024-02-13 12:30:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79213,"visible":true,"origin":"","legend":"\u003cp\u003eCO\u003csub\u003e2 \u003c/sub\u003eemissions in 16 EU countries\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3805125/v1/1475c031902186e88bae0038.png"},{"id":51058841,"identity":"b0f08526-6700-44ce-888a-e6d1593e1fe1","added_by":"auto","created_at":"2024-02-13 12:38:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7992,"visible":true,"origin":"","legend":"\u003cp\u003eTotal Dynamic Connectedness Across 16 EU countries\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3805125/v1/e00f0a697c899939c94b6be5.png"},{"id":51058429,"identity":"28589986-7dac-4331-bc04-19dd6c6348dc","added_by":"auto","created_at":"2024-02-13 12:30:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24294,"visible":true,"origin":"","legend":"\u003cp\u003eCO2 Contributions to Others\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3805125/v1/7ae568cfb9e6d0f91530c060.png"},{"id":51058840,"identity":"d2cd91de-fb80-45d4-bf6a-ea521837147d","added_by":"auto","created_at":"2024-02-13 12:38:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24403,"visible":true,"origin":"","legend":"\u003cp\u003eCO2 Contributions from Others\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3805125/v1/a6c68ec33da30125515a6534.png"},{"id":51058432,"identity":"0e8eb9e0-8a66-473a-9502-8e403581ebeb","added_by":"auto","created_at":"2024-02-13 12:30:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21833,"visible":true,"origin":"","legend":"\u003cp\u003eNet directional total connectedness across 16 EU countries\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3805125/v1/8cf3671729830df344682dc5.png"},{"id":57887649,"identity":"a09918f8-ed90-41fd-a212-c08d11dd54d4","added_by":"auto","created_at":"2024-06-07 05:30:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":973760,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3805125/v1/69a04412-0cfa-494f-84bb-09c1520120fd.pdf"}],"financialInterests":"","formattedTitle":"Dynamic Connectedness and Spillover Effects of CO2 Emissions Among EU Countries: Evidence from the TVP-VAR Connectedness Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change poses a substantial peril to the human race, as it possesses the capacity to disturb critical facets of existence such as food provision, water accessibility, environmental conditions, and the steadiness of marine ecosystems (United Nations, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate change also poses a significant threat to sustainable economic growth through its severe environmental degradation. Hence, it is imperative to conduct a thorough examination of the elements that contribute to climate change in order to identify those that have the potential to disrupt worldwide equilibrium, endanger human life, and cause irreversible damage to the planet (Balsalobre-Lorente et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the negative externalities of continuous economic growth is the release of anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions leading to climate change and environmental degradation (Peng et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since the start of the 21st century, there has been a consistent uptrend in global fossil CO\u003csub\u003e2\u003c/sub\u003e emissions compared to the three previous decades, primarily attributable to fossil fuel combustion (Crippa et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Achieving sustained economic growth, which is widely recognized as essential, hinges on the continuous provision of essential inputs like energy. However, a significant majority of countries depend heavily on nonrenewable energy sources to meet their increasing energy needs, which has unquestionably exacerbated environmental challenges (He et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The rapid trajectory of global climate change is directly associated with the rising trend of global warming and escalating carbon emissions (Doğan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a growing recent literature analyzing environmental pollution spillovers to assess the impact of recent developments in environmental policies to counter climate change. The growing number of studies on the spillovers may be due to the connection between economic growth and environmental pollution; consequently, environmental concerns in one country may impact the policy decisions of other countries (You \u0026amp; Lv, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, closely integrated countries may share a similar development trajectory that results in comparable environmental issues. Hence, it is plausible to hypothesize that the spread of environmental issues from one country to another may significantly impact emission reduction programs in other countries, thereby causing significant environmental degradation concerns (Akram, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven this background, the main objective of the present study is to investigate air pollution spillovers among EU countries. The EU presents an interesting case to study pollution spillovers. First, as the most important example of economic integration, it is one of the largest contributors to global CO\u003csub\u003e2\u003c/sub\u003e emissions (Crippa et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, given this status and to pursue environmental solutions, EU countries aim to become the world\u0026rsquo;s first carbon-neutral continent by 2050 as part of the Green Deal. Thus, the EU Commission has proposed increasing the mandatory renewable sources target in the EU's energy mix to 40% and achieving a 36\u0026ndash;39% reduction in both final and primary energy consumption by 2030 (European Commission, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with these developments, the energy mix of EU countries has shifted significantly over the past two decades. In 2000, for instance, primary energy consumption by fuel types in the EU stood at 26.79 EJ (exajoules) for oil, 12.92 EJ for natural gas, 11.86 EJ for coal, 8.78 EJ for nuclear energy, 3.79 EJ for hydroelectric power, and 0.65 EJ for renewables. By 2022, consumption of oil, natural gas, coal, nuclear energy, and hydroelectric power had fallen, respectively, to 22.13 EJ, 12.36 EJ, 6.98 EJ, 5.48 EJ, and 2.60, whereas consumption of renewable energy had increased to 8.63 EJ. The increase in the share of renewable energy sources within the EU\u0026rsquo;s overall energy mix is remarkable, from merely 1 percent to 14.83 percent at present. Conversely, there has been a notable decline in the share contributed by the main three fossil fuels, oil, coal, and natural gas, from approximately 79.6 percent to around 71.28 percent. Nevertheless, despite ongoing efforts towards reducing carbon emissions and promoting sustainable and environmentally friendly sources of energy, it is evident that reaching the EU\u0026rsquo;s net zero emission target by 2050 remains a considerable challenge, with EU countries currently ranking among the world\u0026rsquo;s top seven emitters (United Nations, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgainst this backdrop, the present paper aims to contribute to the literature on pollution spillover in two respects. First, while some studies on EU countries have used spatial econometric methodologies, this study is the first to analyze pollution spillovers among EU countries using the novel TVP-VAR connectedness approach. This methodology provides a more precise and detailed comprehension of pollution spillovers. By identifying both the countries that receive and transmit pollution spillovers, it is possible to gain insights into the specific dynamics of pollution transmission within the EU. This method also permits us to evaluate the magnitude of spillover and its variation across countries and over time. Second, unlike previous studies, we utilize the CO\u003csub\u003e2\u003c/sub\u003e emissions of the countries originally available at a quarterly frequency, allowing us to draw more robust statistical inferences. By addressing these gaps in existing research, the findings can assist EU policymakers and stakeholders in developing effective strategies to reduce pollution and promote sustainable development.\u003c/p\u003e \u003cp\u003eThe rest of the article is structured as follows. Section two reviews previous research analyzing pollution spillovers among countries, with a specific emphasis on EU countries. Section three describes the methodology of TVP-VAR connectedness. Section four presents the empirical findings using various connectedness measures. Section five summarizes the main findings and suggests some policy implications.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe historical framework indicates that environmental problems have been recognized for over five decades, with various conferences and summits being held, and agreements ratified to address the adverse impacts of the climate crisis. To combat environmental pollution and climate change and reverse the decline in biodiversity rates, countries have committed to achieving carbon neutrality by 2050, according to their treaty endorsements. Given these developments, it is evident that any examination of environmental issues within the empirical economics literature requires comprehensive and more complex methodologies. However, conventional econometric methodologies, which do not allow for interdependencies among the countries or regions, may fail to account for the analysis of pollution spillover effects.\u003c/p\u003e \u003cp\u003eTaking into consideration these factors, several scholars have employed spatial econometric models to analyze pollution spillovers between neighboring countries. Researchers including Zhang et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), You and Lv (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Zhang et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Li et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Abdo et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Gu et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Li and Li (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Murshed et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Li and Wang (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Pea-Assounga and Wu (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Wu et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Jeetoo and Chinyanga (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Qunfang and Huang (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Tawfeeq (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have found positive and significant CO\u003csub\u003e2\u003c/sub\u003e emission spillovers between neighboring countries. Conversely, Al-Silefanee et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Karimi et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) did not find significant spillover while Wen et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Lin et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported negative spillover effects of CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eA few studies have analyzed spillover effects of environmental degradation among EU countries using spatial econometric techniques. For instance, Ren et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported a significant positive spatial spillover of CO\u003csub\u003e2\u003c/sub\u003e emissions from 26 adjacent EU countries to the host country. Focusing on 21 EU countries Radmehr et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that a 1% increase in CO\u003csub\u003e2\u003c/sub\u003e emissions in neighboring countries leads to a 0.06% rise in CO\u003csub\u003e2\u003c/sub\u003e emissions within the host country. Similarly, for 28 EU countries, Shahnazi and Shabani (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that increasing CO\u003csub\u003e2\u003c/sub\u003e emissions in a country\u0026rsquo;s neighboring region results in an increase in the country\u0026rsquo;s own CO\u003csub\u003e2\u003c/sub\u003e emissions. In short, these studies demonstrate that environmental degradation spills over across EU countries.\u003c/p\u003e \u003cp\u003eAlong with the spatial econometric techniques, spillover among the variables has recently been analyzed using a spillover index developed by Diebold and Yilmaz (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). They calculated time-varying spillovers using forecast error decompositions obtained from the rolling estimation of VAR models. Diebold and Yilmaz (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) then extended the DY spillover index by calculating directional spillovers based on generalized forecast error variances independent of the ordering of the variables. Furthermore, Diebold and Yilmaz (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) introduced a connectedness approach based on forecast error variation in different locations due to shocks occurring anywhere and provided ways for assessing connectedness derived from their prior methodologies. These techniques can be used to measure both own effect of the variables and those of others by distinguishing between net shock transmitters and net shock receivers (Antonakakis et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Antonakakis et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) measured connectedness based on the TVP-VAR model instead of rolling window VAR. In this methodology, there is no requirement to set a window size for estimating the VAR model; rather, connectedness measures are calculated from time-varying decomposition without any loss of observations.\u003c/p\u003e \u003cp\u003eIn our review of the literature, only two studies, i.e., Akram (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Shirazi and Šimurina (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), investigating pollution spillovers based on the connectedness approach, were identified. While our study relies on quarterly data for EU countries, Akram (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) employs annual data and analyzes spillover and connectedness of agricultural GHG emissions across continents. While Akram (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) exclusively focuses on GHG emissions, Shirazi and Šimurina (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) employ CO\u003csub\u003e2\u003c/sub\u003e emissions categorized by sector and source from energy consumption in the USA. These studies examined the connectedness of environmental degradation across continents and the USA, respectively.\u003c/p\u003e \u003cp\u003eOur study seeks to fill several research gaps in the literature. Firstly, in contrast to studies employing lower-frequency annual data, our study utilizes CO\u003csub\u003e2\u003c/sub\u003e emissions originally available at a quarterly frequency. This allows us to capture the time-varying nature of environmental degradation, providing a more accurate assessment of spillovers among countries. Secondly, our study targets EU countries. Focusing on the EU countries enables us to derive insights into the effectiveness of international agreements and collaborations, such as the Paris Climate Agreement and the Green New Deal. Our study contributes to the formulation and discussion of necessary policies within the framework of international cooperation. Thirdly, to the best of our knowledge, no studies have investigated CO\u003csub\u003e2\u003c/sub\u003e emission spillovers among EU countries using Antonakakis et al.\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) TVP-VAR connectedness approach, which offers greater robustness and reliability compared to the methodologies employed in previous studies.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study used the TVP-VAR connectedness approach developed by Antonakakis et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) based on Diebold and Yilmaz\u0026rsquo;s (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) connectedness approach. The structure of the estimated TVP-VAR model, allowing for the variance-covariance matrix to change over time, is defined by the following three sets of equations:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({y}_{t}={A}_{t}{z}_{t-1}+{\\epsilon }_{t} {\\epsilon }_{t}\\left|{{\\Omega }}_{t-1}\\sim N\\right.\\)\u003c/span\u003e \u003c/span\u003e(0,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ {\\Sigma }}_{t})\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003e \u003cem\u003evec\u003c/em\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{t})=\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003evec\u003c/em\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{t-1})+{{\\xi }}_{t} {{\\xi }}_{t}\\left|{{\\Omega }}_{t-1}\\sim N \\right.\\)\u003c/span\u003e\u003c/span\u003e(0,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ {\\Xi }}_{t})\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003ewith\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${z}_{t-1}= \\left(\\begin{array}{c}{y}_{t-1}\\\\ {y}_{t-2}\\\\ .\\\\ .\\\\ .\\\\ {y}_{t-p}\\end{array}\\right) {A}_{t}^{{\\prime }}=\\left(\\begin{array}{c}{A}_{1t}\\\\ {A}_{2t}\\\\ .\\\\ .\\\\ .\\\\ {A}_{pt}\\end{array}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Omega }}_{t-1}\\)\u003c/span\u003e\u003c/span\u003e denotes the available information until t-1. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{t}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({z}_{t-1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{t}\\)\u003c/span\u003e\u003c/span\u003erepresents \u003cem\u003em\u003c/em\u003e x 1, \u003cem\u003emp\u003c/em\u003e x 1 and \u003cem\u003em\u003c/em\u003e x 1 vectors, including the current and lagged values of CO\u003csub\u003e2\u003c/sub\u003e emissions respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{t}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003e show the \u003cem\u003em\u003c/em\u003e x \u003cem\u003emp\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e x \u003cem\u003em\u003c/em\u003e dimensional matrices, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\xi }}_{t}\\)\u003c/span\u003e\u003c/span\u003e is the \u003cem\u003em\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e x 1 dimensional vector. Time-varying variance-covariance matrices are denoted by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Sigma }}_{t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Xi }}_{t}\\)\u003c/span\u003e\u003c/span\u003e are with \u003cem\u003em\u003c/em\u003e x \u003cem\u003em\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e x \u003cem\u003em\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e dimensions respectively. Finally, \u003cem\u003evec\u003c/em\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{t})\\)\u003c/span\u003e\u003c/span\u003e represents the vectorized form of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{t}\\)\u003c/span\u003e\u003c/span\u003e with a \u003cem\u003em\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e x 1 dimension.\u003c/p\u003e \u003cp\u003eIn the TVP-VAR connectedness approach, connectedness measures are computed from generalized impulse response functions (GIRF) and generalized forecast error variance decompositions (GFEVD) (Diebold \u0026amp; Yilmaz, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In order to achieve this, the TVP-VAR model is converted into the vector moving average (VMA) form. The representation, based on the Wold theorem, is given by:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${y}_{t}={J}^{{\\prime }}({M}_{t}\\left({z}_{t-2}+{\\eta }_{t-1}\\right)+{n}_{t})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$={J}^{{\\prime }}({M}_{t}\\left({M}_{t}\\left({z}_{t-3}+{\\eta }_{t-2}\\right)+{n}_{t-1}\\right)+{n}_{t})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$⋮$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$={J}^{{\\prime }}({M}_{t}^{k-1}{z}_{t-k-1}+\\sum _{j=0}^{k}{M}_{t}^{j}{\\eta }_{t-j})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewith\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${M}_{t}=\\left(\\begin{array}{cc}{A}_{t}\u0026amp; .\\\\ {I}_{m(p-1)}\u0026amp; {0}_{m\\left(p-1\\right) x m}\\end{array}\\right) {n}_{t}= \\left(\\begin{array}{c}{ϵ}_{t}\\\\ 0\\\\ .\\\\ .\\\\ .\\\\ 0\\end{array}\\right)= J{ϵ}_{t} J=\\left(\\begin{array}{c}I\\\\ 0\\\\ .\\\\ .\\\\ .\\\\ 0\\end{array}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the \u003cem\u003emp\u003c/em\u003e x \u003cem\u003epm\u003c/em\u003e dimensional matrix whereas \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(J\\)\u003c/span\u003e\u003c/span\u003e denotes the \u003cem\u003emp\u003c/em\u003e x \u003cem\u003em\u003c/em\u003e dimensional matrix and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({n}_{t}\\)\u003c/span\u003e\u003c/span\u003e stands for the \u003cem\u003emp\u003c/em\u003e x 1 dimensional vector.\u003c/p\u003e \u003cp\u003eTaking the limit as k approaches\u0026thinsp;\u0026infin;\u0026thinsp;gives the following:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${y}_{t}=\\underset{k\\to {\\infty }}{\\text{lim}}{J}^{{\\prime }}({M}_{t}^{k-1}{z}_{t-k-1}+\\sum _{j=0}^{k}{M}_{t}^{j}{\\eta }_{t-j})=\\sum _{j=0}^{{\\infty }}{{J}^{{\\prime }}M}_{t}^{j}{\\eta }_{t-j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${y}_{t}=\\sum _{j=0}^{{\\infty }}{{J}^{{\\prime }}M}_{t}^{j}J{ϵ}_{t-j} {B}_{jt}= {{J}^{{\\prime }}M}_{t}^{j}J, j=\\text{0,1},\\dots$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$${y}_{t}=\\sum _{j=0}^{{\\infty }}{B}_{jt}{ϵ}_{t-j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({B}_{jt}\\)\u003c/span\u003e\u003c/span\u003e indicates an m x m dimensional matrix.\u003c/p\u003e \u003cp\u003eThe GIRFs (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\psi }_{ij,t}\\left(H\\right))\\)\u003c/span\u003e\u003c/span\u003e show the responses of all variables \u003cem\u003ej\u003c/em\u003e following a shock in variable \u003cem\u003ei\u003c/em\u003e. Here, the differences between an H-step-ahead forecast are calculated with and without variable \u003cem\u003ei\u003c/em\u003e being shocked.\u003c/p\u003e \u003cp\u003eThis can be computed in the following manner:\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$${GIRF}_{t}\\left(H,{\\delta }_{j,t},{{\\Omega }}_{t-1}\\right)=E\\left({y}_{t+H}|{e}_{j}={\\delta }_{j,t},{{\\Omega }}_{t-1}\\right)-E({y}_{t+J}│{{\\Omega }}_{t-1})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$${\\psi }_{j,t}\\left(H\\right)=\\frac{{B}_{H,t}{\\sum }_{t}{e}_{j}}{\\sqrt{{\\sum }_{jj,t}}}\\frac{{\\delta }_{j,t}}{\\sqrt{{\\sum }_{jj,t}}} {\\delta }_{j,t}= \\sqrt{{\\sum }_{jj,t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$${\\psi }_{j,t}\\left(H\\right)={\\sum }_{jj,t}^{- \\frac{1}{2}}{B}_{H,t}{\\sum }_{t}{e}_{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({e}_{j}\\)\u003c/span\u003e\u003c/span\u003e is an \u003cem\u003em\u003c/em\u003e x 1 selection vector with unity in the \u003cem\u003ejth\u003c/em\u003e rank, and zero otherwise. GFEVD(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\stackrel{\\sim}{\\varphi }}_{ij,t}\\left(H\\right))\\)\u003c/span\u003e\u003c/span\u003e is pairwise directional connectedness from \u003cem\u003ej\u003c/em\u003e to \u003cem\u003ei\u003c/em\u003e. It represents the effect variable \u003cem\u003ej\u003c/em\u003e has on variable \u003cem\u003ei\u003c/em\u003e. It is measured as:\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$${\\stackrel{\\sim}{\\varphi }}_{ij,t}\\left(H\\right)=\\frac{\\sum _{t=1}^{H-1}{\\psi }_{ij,t}^{2}}{\\sum _{j=1}^{m}\\sum _{t=1}^{H-1}{\\psi }_{ij,t}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e15\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{j=1}^{m}{\\stackrel{\\sim}{\\varphi }}_{ij,t}\\left(H\\right)\\)\u003c/span\u003e\u003c/span\u003e = 1 and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i,j=1}^{m}{\\stackrel{\\sim}{\\varphi }}_{ij,t}\\left(H\\right)\\)\u003c/span\u003e\u003c/span\u003e = \u003cem\u003em\u003c/em\u003e. In Eq.\u0026nbsp;\u003cspan refid=\"Equ13\" class=\"InternalRef\"\u003e15\u003c/span\u003e, the denominator indicates the cumulative impact of all the shocks, whereas the numerator shows the cumulative impact of a shock of in variable \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eUsing Eq.\u0026nbsp;\u003cspan refid=\"Equ13\" class=\"InternalRef\"\u003e15\u003c/span\u003e, the total connectedness index is constructed as follows:\u003cdiv id=\"Equ14\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ14\" name=\"EquationSource\"\u003e\n$${C}_{t}\\left(H\\right)=\\frac{\\sum _{i, j=1, i\\ne j }^{m}{\\stackrel{\\sim}{\\varnothing }}_{ij,t}\\left(H\\right)}{\\sum _{i, j=1}^{m}{\\stackrel{\\sim}{\\varnothing }}_{ij,t}\\left(H\\right)}*100=\\frac{\\sum _{i, j=1, i\\ne j }^{m}{\\stackrel{\\sim}{\\varnothing }}_{ij,t}\\left(H\\right)}{m}*100.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e16\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis connectedness index shows how a shock that occurred in one variable spills over to other variables. If variable \u003cem\u003ei\u003c/em\u003e transmits its shock to all other variables \u003cem\u003ej\u003c/em\u003e, it is labelled \u0026ldquo;total directional connectedness to others\u003cem\u003e\u0026rdquo;\u003c/em\u003e and calculated as follows:\u003cdiv id=\"Equ15\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ15\" name=\"EquationSource\"\u003e\n$${C}_{i\\to \\text{j},\\text{t}}\\left(H\\right)=\\frac{\\sum _{j=1, i\\ne j }^{m}{\\stackrel{\\sim}{\\varnothing }}_{ij,t}\\left(H\\right)}{\\sum _{j=1}^{m}{\\stackrel{\\sim}{\\varnothing }}_{ij,t}\\left(H\\right)}*100.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e17\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhen variable \u003cem\u003ei\u003c/em\u003e receives shocks from all variables \u003cem\u003ej\u003c/em\u003e, it is labelled \u0026ldquo;total directional connectedness from others\u0026rdquo; and calculated as follows:\u003cdiv id=\"Equ16\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ16\" name=\"EquationSource\"\u003e\n$${C}_{i\\leftarrow \\text{j},\\text{t}}\\left(H\\right)=\\frac{\\sum _{j=1, i\\ne j }^{m}{\\stackrel{\\sim}{\\varnothing }}_{ij,t}\\left(H\\right)}{\\sum _{i=1}^{m}{\\stackrel{\\sim}{\\varnothing }}_{ij,t}\\left(H\\right)}*100.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e18\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;Net total directional connectedness,\u0026rdquo; which is the difference between total directional connectedness to others and total directional connectedness from others, illustrates the impact of variable \u003cem\u003ei\u003c/em\u003e on the network. It is written as follows:\u003cdiv id=\"Equ17\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ17\" name=\"EquationSource\"\u003e\n$${C}_{i,t}={C}_{i\\to \\text{j},\\text{t}}\\left(H\\right)-{C}_{i\\leftarrow \\text{j},\\text{t}}\\left(H\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e19\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIf \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e is positive, variable \u003cem\u003ei\u003c/em\u003e affects the network more than being affected itself. Conversely, if it is negative, variable \u003cem\u003ei\u003c/em\u003e is influenced by the network. Lastly, net total directional connectedness can be broken down to measure bidirectional relationships by calculating \u0026ldquo;net pairwise directional connectedness.\u0026rdquo;\u003cdiv id=\"Equ18\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ18\" name=\"EquationSource\"\u003e\n$${NPDC}_{ij }\\left(H\\right)={\\stackrel{\\sim}{\\varnothing }}_{jit}\\left(H\\right)-{\\stackrel{\\sim}{\\varnothing }}_{jit}\\left(H\\right))*100.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e20\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIf \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({NPDC}_{ij }\\left(H\\right)\\)\u003c/span\u003e\u003c/span\u003e is higher than zero, it indicates that variable \u003cem\u003ei\u003c/em\u003e dominates variable \u003cem\u003ej\u003c/em\u003e. When it is lower than zero, it indicates that variable \u003cem\u003ej\u003c/em\u003e dominates variable \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e"},{"header":"4. Dataset","content":"\u003cp\u003eThis study aimed to estimate dynamic connectedness and spillover of CO\u003csub\u003e2\u003c/sub\u003e emissions among 16 EU countries. To do so, quarterly CO\u003csub\u003e2\u003c/sub\u003e emissions data from 1980Q1 to 2023Q3 were derived from the Refinitiv Eikon Datastream (2023) database. The countries and the period were selected based on data availability. The 16 sampled EU countries were Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Romania, Spain, Sweden, and the United Kingdom.[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e CO\u003csub\u003e2\u003c/sub\u003e emissions were converted to their natural logarithm before the analysis.\u003c/p\u003e \u003cp\u003eDescriptive statistics for the CO\u003csub\u003e2\u003c/sub\u003e emissions are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that CO\u003csub\u003e2\u003c/sub\u003e emissions have decreased synchronously in the 16 countries. Germany is the largest CO\u003csub\u003e2\u003c/sub\u003e emitter, followed by the United Kingdom, Italy, and France. Notably, the COVID-19 outbreak resulted in a significant decrease in emissions in 2020, which can be attributed to various restrictive measures, such as stay-at-home directives, lockdown protocols, travel limitations, border closures impacting trade, and a subsequent decrease in production. However, the EU\u0026rsquo;s CO\u003csub\u003e2\u003c/sub\u003e emissions surged after the COVID-19 pandemic ended due to a return to pre-pandemic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEx.Kurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ(10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e(10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.861***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.459*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e799.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e799.014***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.734***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.478***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e618.318***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e626.894***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.623***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.936***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.705***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e832.223***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e842.606***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.975***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.909***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e785.338***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e767.767***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.301*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e702.062***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e686.967***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.666***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.128***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e630.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e627.832***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.321*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.858*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e800.519***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e803.958***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.045***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.366***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e819.930***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e839.063***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.944***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.774**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e865.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e870.284***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.445**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.255**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e798.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e807.339***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.620***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142.935***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e615.479***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e635.284***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.682***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.698***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.130***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e818.652***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e830.337***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.439**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.230***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.642***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e875.589***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e878.979***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.885***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.248**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e839.118***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e843.590***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.540***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.594**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.062***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e769.782***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e753.306***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.474***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.108**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.318***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e777.582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e787.142***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: *, **, and *** denote statistical significance at 10%, 5%, and 1% level, respectively. JB stands for the Jarque and Bera (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) normality test. Skewness and kurtosis were calculated using the methods of D\u0026rsquo;Agostino (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) and the Anscombe and Glynn (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) statistics. Q(10) and Q\u003csup\u003e2\u003c/sup\u003e(10) represent the weighted Ljung-Box statistic for assessing serial correlation in CO\u003csub\u003e2\u003c/sub\u003e emissions and its squared value, as proposed by Fisher and Gallagher (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here\u0026gt;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnit Root Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrend and Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrend and Intercept\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.5499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.4443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.2265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.1173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.1068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.1026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.9047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.2169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.0842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.6159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.5721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.4116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.5291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.4001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.7018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.3861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.0539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.4771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.4559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.9935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.4956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.5139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.1373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.0617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.1956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.0585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.4498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.1475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.3888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.4254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.7252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.7602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.5889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eADF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrend and Intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrend and Intercept\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.8448***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.7891***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.4339***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.458***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.9789**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.3433*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.6952**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.2774*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.8225***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.82***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.6272***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.5784***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.891***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.7973***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.3146***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.2862***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.3968***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.8409***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.9515***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.8314***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.569***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.5431***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.9414***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.898***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.382***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.4314***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.4911***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.6035***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.8133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.6355**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.5587**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.6888***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.8797***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.6951***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.8596***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.866***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.5483***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.8608***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.379***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.6934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.2865*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.2917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.6534*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔPortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14.468***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14.7551***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.4436***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.9856***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.3156***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.3011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.417***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.4001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.2618***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.3903***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.2618***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.3903***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.2247***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.444***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.166***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.1159***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16.8056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-17.0111***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-17.6516***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-21.0604***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The lag length for the ADF test was selected based on the Schwarz information criterion (SIC). The PP test was estimated on the basis of the Bartlett\u0026ndash;Kernel test, using the Newey\u0026ndash;West bandwidth. The null hypothesis is that the series are nonstationary. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistics in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reveal that CO\u003csub\u003e2\u003c/sub\u003e emissions vary significantly among the 16 countries. For example, Belgium has the lowest variance, while Romania has the highest. Regarding mean values, Germany, the UK, Italy, and France have the highest mean CO\u003csub\u003e2\u003c/sub\u003e emissions levels while Denmark, Portugal, and Ireland have the lowest. The CO\u003csub\u003e2\u003c/sub\u003e emissions of Belgium, Denmark, Finland, France, Germany, Italy, the Netherlands, Portugal, Sweden, and the UK are significantly negatively skewed whereas those of Bulgaria and Romania are significantly positively skewed. Austria, Bulgaria, Greece, Ireland, Portugal, Romania, Spain, and Sweden have platykurtic distributions whereas France, the Netherlands, and the UK have notably leptokurtic distributions. Based on the Jarque-Bera test, only Finland\u0026rsquo;s emissions data follow a normal distribution. The results of the Q(10) and Q\u003csup\u003e2\u003c/sup\u003e(10) tests indicate autocorrelation among emissions while the unit root test results shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicate that all the series exhibit stationarity at their first difference. Therefore, all the series are integrated of order one (I(1)). The evidence of the non-stationarity of all variables suggests that CO\u003csub\u003e2\u003c/sub\u003e emissions should be included in their first difference form in the TVP-VAR estimation.\u003c/p\u003e"},{"header":"5. Empirical Findings","content":"\u003cp\u003eAfter defining the time series properties of the CO\u003csub\u003e2\u003c/sub\u003e emissions, the TVP-VAR model defined by equations (1), (2), and (3) was estimated, and the connectedness measures defined in the \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003emethodology\u003c/span\u003e section were calculated to analyze pollution spillovers among the 16 EU countries. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results from the dynamic total connectedness measure defined by Eq.\u0026nbsp;(\u003cspan refid=\"Equ14\" class=\"InternalRef\"\u003e16\u003c/span\u003e). As is evident from the black-shaded area, the countries are interconnected, with the extent of connectedness ranging between 68% and 92%. Hence, it can be inferred that pollution spillover among these EU countries is substantial and varies considerably over time. The connectedness fell in 2009 due to the financial crisis, which led to bankruptcies and reduced production. This in turn reduced CO\u003csub\u003e2\u003c/sub\u003e emissions because higher production levels are typically associated with increased pollution. This finding is supported by Peters et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who concluded that the global financial crisis only affected emissions for a short time due to the decline in production-based emissions and a significant decrease in international trade, stemming from the declining trend of consumption-based emissions. In particular, Declercq et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) highlighted that the economic downturn in 2008 led to a substantial decrease in economic activity. Industrial activity, as well as electricity and fuel demand, fell significantly. Conversely, emissions rose as countries emerged from the crisis due to increasing trade, production, and growth levels, and consequently greater connectedness. Connectedness also decreased in 2020 due to the COVID-19 outbreak, leading to a fall in CO\u003csub\u003e2\u003c/sub\u003e emissions whereas it increased after COVID-19 restrictions were lifted. In alignment with our findings, Qu\u0026eacute;r\u0026eacute; et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argued that the temporary measures adopted by countries have only a minimal influence on the prevailing fuel-based structure across all countries. Consequently, the fundamental causes behind emissions resurfaced. Similarly, Nguyen et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argued that substantial falls in output, transportation, and energy demand due to the limited movement of individuals during the pandemic led to significant drops in emissions of greenhouse gases, such as CO\u003csub\u003e2\u003c/sub\u003e. These arguments are also valid for EU countries (Jawadi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that connectedness after the COVID-19 outbreak seems to have become stronger, with countries\u0026rsquo; emissions spilling over and affect themselves.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here\u0026gt;\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the connectedness of each country to identify the emissions transmitters and receivers among the 16 EU countries. The TCI value is 75.45, indicating a strong connectedness relationship between these countries. Particularly notable co-movements are observed between Belgium and Germany, Bulgaria and Romania, Denmark and Finland, Ireland and the UK, Italy, and Spain, and Portugal and Spain. Interestingly, these co-movements may have occurred because these countries are also close geographically. Regarding their contributions to others, the UK, Germany, Italy, and France exhibit the highest spillover effects on other countries, with values of 96.64, 95.02, 93.26, 91.39, respectively. Conversely, the UK and France are significantly influenced by other countries, with values of 81.49 and 80.93, respectively. In terms of net connectedness measures, Germany and the UK emerge as the main CO\u003csub\u003e2\u003c/sub\u003e transmitters within the network, with net values of 15.26 and 15.15, respectively. Conversely, Greece and Bulgaria are the main receivers, with net values of -30.34 and \u0026minus;\u0026thinsp;14.85, respectively. Interestingly, the GDP per capita values of the main transmitter countries are higher than those of the receiver countries.[2]\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e That is, higher income countries tend to generate and transmit more pollution. Finally, Austria, Belgium, France, Ireland, Italy, Portugal, and Spain are identified as transmitter countries within this network whereas Denmark, Finland, the Netherlands, Romania, and Sweden are identified as receiver countries.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDynamic Connectedness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eFrom\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e77.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e78.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e17.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e67.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e72.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e71.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e9.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e80.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e79.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e71.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e21.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e12.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e78.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e11.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e79.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e21.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e8.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e78.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e27.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e72.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e35.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e64.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e20.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e79.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e27.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e72.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e81.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e80.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e93.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e73.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e73.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e52.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e84.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e65.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e96.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1207.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-30.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-12.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e15.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eTCI:75.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eNote: TCI stands for Total Connectedness Index. A positive net value indicates that a country is a net transmitter of carbon emissions while a negative net value indicates that it is a net receiver.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e about here\u0026gt;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e display how connectedness measures varied over the analysis period. The results show that pollution spillovers were time-varying in that all 16 countries have been both transmitters and receivers at some point. That is, carbon emissions within the EU certainly spill over, affecting each country\u0026rsquo;s own emissions and environmental policies. These spillover findings are similar to those of Akram (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Shirazi and Šimurina (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who applied the DY methodology. For EU countries, the existence of spillover is supported by the findings of Ren et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Radmehr et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Shahnazi and Shabani (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who used spatial econometric models. of the presence of spillover and connectedness indicates that the EU countries must collaborate when designing policies aimed at mitigating environmental degradation. These policy implications will be discussed in the \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003econclusion\u003c/span\u003e section.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn contrast to previous studies using spatial techniques and methods based on the DY methodology, to the best of our knowledge, our study is the first to apply the methodology introduced by Antonakakis et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to EU countries using quarterly CO\u003csub\u003e2\u003c/sub\u003e emissions data. The EU countries are of great significance as they are at the forefront of the global climate and energy crises, with the capacity to influence environmental issues globally. Accordingly, we analyzed the spillover effects and dynamic connectedness measures of 16 EU countries.\u003c/p\u003e \u003cp\u003eOur findings indicate that the UK, Germany, Italy, and France have the highest spillover effects on other countries. Given that these countries also have the highest CO\u003csub\u003e2\u003c/sub\u003e emissions, this finding is important and interesting. Regarding net connectedness measures, Germany and the UK are the primary CO\u003csub\u003e2\u003c/sub\u003e transmitters whereas Greece and Bulgaria are the main receivers. These findings align with previous observations that countries with higher incomes tend to contribute more to pollution spillovers whereas countries with lower incomes tend to suffer more from spillovers. The dynamic connectedness between pairs of countries also reveals that the closer countries are geographically, the more interconnected they tend to be.\u003c/p\u003e \u003cp\u003eThese findings have four significant policy consequences. First, they indicate that environmental problems are not confined to one country and cannot be solved in isolation due to spillover effects among countries. This conclusion is also supported by United Nations, which maintains that addressing climate change requires an unprecedented level of global cooperation. Therefore, countries need to question their economic models, invent new industries, and value nature far beyond money to force rich nations to recognize their moral responsibility to the rest of the world (United Nations, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to the existence of spillover effects, the EU countries should collaborate in developing carbon mitigation technologies, share the associated costs, and provide incentives to firms or member countries that develop patents to address environmental issues while also raising the expenses linked to higher emissions.\u003c/p\u003e \u003cp\u003eSecond, as higher-income countries are the primary pollution transmitters, there is a trade-off between economic growth and environmental preservation. However, sustainable development necessitates both economic progress and environmental protection. Therefore, it is critical to decouple growth from environmental degradation at this juncture, as emphasized in the 8th Sustainable Development Goal (UNDP, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To ensure environmental protection, the EU countries must strive to achieve the goal of increasing the share of renewables and completely phasing out nonrenewable sources as soon as possible.\u003c/p\u003e \u003cp\u003eThird, as connectedness is stronger if countries are closer geographically, these countries must carefully consider the environmental consequences of their actions and policies. In particular, when formulating policies, they should be aware that they affect not only to themselves but also neighboring countries.\u003c/p\u003e \u003cp\u003eFourth, even though various conferences, summits, and ratified agreements emphasize the key role of international cooperation, such as the EU\u0026rsquo;s inclusion in the Paris Agreement and the Green New Deal, more than mere highlighting is required. Concrete actions are needed, commitments must be taken seriously, and targets must be achieved promptly. Awareness of these points will ensure a sustainable world with a clean environment and economic development.\u003c/p\u003e \u003cp\u003eIn the present study, only CO\u003csub\u003e2\u003c/sub\u003e emissions were used as a proxy for environmental pollution due to the limited availability of high-frequency data for other indicators. However, carbon emissions only reflect air pollution and ignore soil and water pollution. To overcome this drawback, future studies could incorporate other indicators, such as the ecological footprint. Finally, future studies could conduct a similar analysis for the top large emitting countries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e[Competing Interests: none, no conflict of interest]\u003c/p\u003e\n\u003cp\u003e[Acknowledgements: none, no funds were received]\u003c/p\u003e\n\u003cp\u003e[Funding: none, not applicable]\u003c/p\u003e\n\u003cp\u003e[Ethical approval: This article does not contain any studies with human participants performed by any of the authors]\u003c/p\u003e\n\u003cp\u003eConsent for publication: Our study does not contain individual person\u0026rsquo;s data.\u003c/p\u003e\n\u003cp\u003eConsent to Participate: No human or animal subjects were used in our study, and no questionnaire was conducted.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAuthors Contributions: Cagla Bucak: Data Curation, Methodology, Literature Review, Writing Original Draft, Formal Analysis; Abdurrahman Nazif \u0026Ccedil;atık: Conceptualization, Software, Writing Original Draft, Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdo, A.-B., Li, B., Zhang, X., Lu, J., \u0026amp; Rasheed, A. 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A spatial econometric analysis. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, \u003cem\u003e202\u003c/em\u003e, 510\u0026ndash;523. https://doi.org/10.1016/j.jclepro.2018.08.146\u003c/li\u003e\n\u003cli\u003eZhang, Q., Yang, J., Sun, Z., \u0026amp; Wu, F. (2017). Analyzing the impact factors of energy-related CO2 emissions in China: What can spatial panel regressions tell us? \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e, 1085\u0026ndash;1093. https://doi.org/10.1016/j.jclepro.2017.05.071\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Even though the UK left the EU in 2020, we included it because, between 1980 and 2020, as well as after 2020, its emissions may have influenced the environmental problems of the EU countries.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We obtained GDP per capita (constant 2010 US\u003cspan\u003e$\u003c/span\u003e) data from Refinitiv Eikon DataStream (2023), with the latest data being applicable to 2022. According to the 2022 data, Greece and Bulgaria have income values of 20,167 and 9,502, respectively. In contrast, the UK\u0026rsquo;s and Germany\u0026rsquo;s income values are 47,232 and 43,032, respectively.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spillover, TVP-VAR connectedness, CO2 emissions, EU","lastPublishedDoi":"10.21203/rs.3.rs-3805125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3805125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores spillover effects of carbon emissions among the 16 EU countries from 1980Q1 to 2023Q3, employing the TVP-VAR connectedness methodology introduced by Antonakakis et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The findings reveal high connectedness, i.e. substantial spillover among the EU countries. Regarding net connectedness measures, the main transmitters of CO\u003csub\u003e2\u003c/sub\u003e emissions are Germany and the UK whereas the main receivers are Greece and Bulgaria. This high connectedness underscores the importance of collaborative efforts among EU countries in formulating policies to mitigate environmental degradation. The findings also indicate a positive correlation between economic activity and pollution, with higher-income countries tending to contribute more to pollution spillover. Our results further suggest that EU member states should endeavor to increase the use of renewable energy sources while phasing out nonrenewable ones, in accordance with the overarching objective of environmental protection, which is to ensure effective environmental protection.\u003c/p\u003e","manuscriptTitle":"Dynamic Connectedness and Spillover Effects of CO2 Emissions Among EU Countries: Evidence from the TVP-VAR Connectedness Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 12:30:16","doi":"10.21203/rs.3.rs-3805125/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03f4e6b8-e98b-45d1-ad87-10f6d0b81524","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-07T05:21:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-13 12:30:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3805125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3805125","identity":"rs-3805125","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}