jepr İktisat Politikası Araştırmaları Dergisi 2148-3876 İstanbul Üniversitesi 10.26650/JEPR1704245 Macroeconomics (Other) Makro İktisat (Diğer) A Time-Varying Analysis of Pollution Spillovers Among EU Countries: Evidence from a TVP-VAR Connectedness Approach https://orcid.org/0000-0003-3169-110X Bucak Çağla EGE UNIVERSITY https://orcid.org/0000-0001-9247-5668 Çatık Abdurrahman EGE UNIVERSITY 02 19 2026 13 1 109 128 05 22 2025 01 22 2026 Copyright © 2014, İktisat Politikası Araştırmaları Dergisi 2014 İktisat Politikası Araştırmaları Dergisi

This study explores the spillover effects of carbon emissions among the 16 EU countries from 1980Q1 to 2023Q3, employing the TVP-VAR connectedness methodology. Spillovers are calculated based on the time-varying forecast error variance decompositions of CO₂ emissions for each country. As CO₂ emissions for all countries are integrated of order one, first differences are employed in the analysis. The findings reveal a high level of connectedness among EU countries, with values ranging from 68% to 92% and a Total Connectedness Index of 75.45. Regarding net connectedness, Germany and the UK emerge as the main CO₂ transmitters, with net values of 15.26 and 15.15, respectively, while Greece and Bulgaria are the main receivers, with net values of −30.34 and −14.85. This high connectedness underscores the importance of collaborative efforts among EU countries in developing 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 endeavour to increase the use of renewable energy sources while phasing out nonrenewable ones, in accordance with the overarching objective of environmental protection.

 Spillover TVP-VAR connectedness Transmitter Receiver CO2 emissions EU Abdo, A.-B., Li, B., Zhang, X., Lu, J., & Rasheed, A. (2020). Influence of FDI on environmental pollution in selected Arab countries: a spatial econometric analysis perspective. Environmental Science and Pollution Research, 27, 28222-28246. [https://doi.org/10.1007/s11356-020-08810-4](https://doi.org/10.1007/s11356-020-08810-4). google scholar Akhundjanov, S., & Muñoz-García, F. (2019). Transboundary Natural Resources, Externalities, and Firm Preferences for Regulation. Environmental and Resource Economics, 7, 333–352 [https://doi.org/10.1007/s10640-018-0265-5](https://doi.org/10.1007/s10640-018-0265-5). google scholar Akram, V. (2022). Spillover effect of greenhouse gas emissions across five major continents. Environmental Science and Pollution Research, 29, 11634-11643. [https://doi.org/10.1007/s11356-021-16535-1](https://doi.org/10.1007/s11356-021-16535-1). google scholar Al-Silefanee, R. R., Mamkhezri, J., Khezri, M., Karimi, M. S., & Khan, Y. A. (2022). Effect of Islamic financial development on carbon emissions: A spatial econometric analysis. Frontiers in Environmental Science, 10, 1-13. [https://doi.org/10.3389/fenvs.2022.850273](https://doi.org/10.3389/fenvs.2022.850273). google scholar Alola A. A., & Bekun F. V. (2021). Pandemic outbreaks (COVID-19) and sectoral carbon emissions in the United States: A spillover effect evidence from Diebold and Yilmaz index. Energy & Environment, 32 (5), 945-955. [https://doi.org/10.1177/0958305X20977275](https://doi.org/10.1177/0958305X20977275). google scholar Anscombe, F. J., & Glynn W. J. (1983). Distribution of the kurtosis statistic b2 for normal samples. Biometrika, 70 (1), 227-234. google scholar Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13 (84). [https://doi.org/10.3390/jrfm13040084](https://doi.org/10.3390/jrfm13040084). google scholar Balsalobre-Lorente, D., Ibáñez-Luzón, L., Usman, M., & Shahbaz, M. (2022). The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renewable Energy, 185, 1441-1455. [https://doi.org/10.1016/j.renene.2021.10.059](https://doi.org/10.1016/j.renene.2021.10.059). google scholar Barrett, S. (1994). Self-Enforcing International Environmental Agreements. Oxford Economic Papers, 46, 878-894. google scholar Cohen, G., Jalles, J. T., Loungani, P., Pizzuto, P. (2022). Trends and cycles in CO2 emissions and incomes: Cross-country evidence on decoupling. Journal of Macroeconomics, 71(103397). [https://doi.org/10.1016/j.jmacro.2022.103397](https://doi.org/10.1016/j.jmacro.2022.103397). google scholar Crippa, M., Guizzardi, D., Banja, M., Solazzo, E., Muntean, M., Schaaf, E. … Vignati, E. (2022). CO2 emissions of all world countries. EU Publications. [https://doi.org/10.2760/730164](https://doi.org/10.2760/730164). google scholar D’Agostino, R. B. (1970). Transformation to normality of the null distribution of g1. Biometrika, 57 (3), 679-681. google scholar Declercq, B., Delarue, E., & D’haeseleer, W. (2011). Impact of the economic recession on the European power sector’s CO2 emissions. Energy Policy, 39, 1677–1686. [https://doi.org/10.1016/j.enpol.2010.12.043](https://doi.org/10.1016/j.enpol.2010.12.043). google scholar Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119, 158-171. [https://doi.org/10.1111/j.1468-0297.2008.02208.x](https://doi.org/10.1111/j.1468-0297.2008.02208.x). google scholar Diebold, F. X. & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28, 57-66. [https://doi.org/10.1016/j.ijforecast.2011.02.006](https://doi.org/10.1016/j.ijforecast.2011.02.006). google scholar Diebold, F. X. & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182, 119–134. [https://doi.org/10.1016/j.jeconom.2014.04.012](https://doi.org/10.1016/j.jeconom.2014.04.012). google scholar Doda, B. (2014). Evidence on business cycles and CO2 emissions. Journal of Macroeconomics, 40, 214-227. [http://dx.doi.org/10.1016/j.jmacro.2014.01.003](http://dx.doi.org/10.1016/j.jmacro.2014.01.003). google scholar Doğan, B., Driha, O. M., Balsalobre Lorente, D., & Shahzad, U. (2021). The mitigating effects of economic complexity and renewable energy on carbon emissions in developed countries. Sustainable Development, 29, 1-12. [https://doi.org/10.1002/sd.2125](https://doi.org/10.1002/sd.2125). google scholar European Commission. (2020). Delivering the European Green Deal. [Retrieved 2023-09-18] Available at: [https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal/delivering-european-green-deal_en](https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal/delivering-european-green-deal_en). google scholar Fisher, T. J. & Gallagher, C. M. (2012). New weighted portmanteau statistics for time series goodness of fit testing. Journal of the American Statistical Association, 107 (498), 777-787. [https://doi.org/10.1080/01621459.2012.688465](https://doi.org/10.1080/01621459.2012.688465). google scholar Gu, W., Chu, Z., & Wang, C. (2020). How do different types of energy technological progress affect regional carbon intensity? A spatial panel approach. Environmental Science and Pollution Research, 27, 44494-44509. [https://doi.org/10.1007/s11356-020-10327-9](https://doi.org/10.1007/s11356-020-10327-9). google scholar Ha, L. T. (2023). Scrutinizing interlinkages between digitalization, economic complexity, green technologies, green energy consumption and CO2 emission by quantile spillovers in Vietnam. Environmental Science and Pollution Research, 30, 81073-81092. [https://doi.org/10.1007/s11356-023-28114-7](https://doi.org/10.1007/s11356-023-28114-7). google scholar He, K., Ramzan, M., Awosusi, A. A., Ahmed, Z., Ahmed, Z., & Altuntaş, M. (2021). Does globalization moderate the effect of economic complexity on CO2 emissions? Evidence from the top 10 energy transition economies. Frontiers in Environmental Science, 9 (778088). [https://doi.org/10.3389/fenvs.2021.778088](https://doi.org/10.3389/fenvs.2021.778088). google scholar Jarque, C. M., & Bera, A. K. (1980). “Efficient tests for normality, homoscedasticity and serial ındependence of regression residuals.” Economics Letters, 6, 255-259. google scholar Jawadi, F., Rozin, P., & Bourghelle, D. (2023). Insights into CO2 emissions in Europe in the context of COVID-19: A panel data analysis. International Economics, 173, 164-174. [https://doi.org/10.1016/j.inteco.2022.11.006](https://doi.org/10.1016/j.inteco.2022.11.006). google scholar Jebabli, I., Lahiani, A., & Mefteh-Wali, S. (2023). Quantile connectedness between CO2 emissions and economic growth in G7 countries. Resources Policy, 81 (103348). [https://doi.org/10.1016/j.resourpol.2023.103348](https://doi.org/10.1016/j.resourpol.2023.103348). google scholar Jeetoo, J., & Chinyanga, E. R. (2023). A spatial econometric analysis of the environment Kuznets curve and pollution haven hypothesis in Sub-Saharan Africa. Environmental Science and Pollution Research, 30, 58169-58188. [https://doi.org/10.1007/s11356-023-26306-9](https://doi.org/10.1007/s11356-023-26306-9). google scholar Kanas, A., Molyneux, P., & Zervopoulos, P. D. (2023). Systemic risk and CO2 emissions in the U.S. Journal of Financial Stability, 64 (101088). [https://doi.org/10.1016/j.jfs.2022.101088](https://doi.org/10.1016/j.jfs.2022.101088). google scholar Karimi, M. S., Khezri, M., Khan, Y. A., & Razzaghi, S. (2022). Exploring the influence of economic freedom index on fishing grounds footprint in environmental Kuznets curve framework through spatial econometrics technique: evidence from Asia-Pacific countries. Environmental Science and Pollution Research, 29, 6251-6266. [https://doi.org/10.1007/s11356-021-16110-8](https://doi.org/10.1007/s11356-021-16110-8). google scholar Li, J., & Li, S. (2020). Energy investment, economic growth and carbon emissions in China-Empirical analysis based on spatial Durbin model. Energy Policy, 140 (111425). [https://doi.org/10.1016/j.enpol.2020.111425](https://doi.org/10.1016/j.enpol.2020.111425). google scholar Li, K., Fang, L., & He, L. (2019). The impact of energy price on CO2 emissions in China: A spatial econometric analysis. Science of the Total Environment, 706 (135942). [https://doi.org/10.1016/j.scitotenv.2019.135942](https://doi.org/10.1016/j.scitotenv.2019.135942) google scholar Li, Z., & Wang, J. (2022). Spatial spillover effect of carbon emission trading on carbon emission reduction: Empirical data from pilot regions in China. Energy, 251 (123906). [https://doi.org/10.1016/j.energy.2022.123906](https://doi.org/10.1016/j.energy.2022.123906). google scholar Libecap, G. D. (2013). Addressing Global Environmental Externalities: Transaction Costs Considerations. NBER Working Paper No. 19501. google scholar Lin, H., Wang, X., Bao, G., & Xiao, H. (2022). Heterogeneous Spatial Effects of FDI on CO2 Emissions in China. Earth’s Future, 10. [https://doi.org/10.1029/2021EF002331](https://doi.org/10.1029/2021EF002331). google scholar Mbarek, M. B., Ali, N. B., & Feki, R. (2014). Causality relationship between CO2 emissions, GDP and energy intensity in Tunisia. Environment, Development and Sustainability, 16, 1253-1262. [https://doi.org/10.1007/s10668-014-9525-x](https://doi.org/10.1007/s10668-014-9525-x). google scholar Murshed, M., Nurmakhanova, M., Elheddad, M., & Ahmed, R. (2020). Value addition in the services sector and its heterogeneous impacts on CO2 emissions: revisiting the EKC hypothesis for the OPEC using panel spatial estimation techniques. Environmental Science and Pollution Research, 27, 38951-38973. [https://doi.org/10.1007/s11356-020-09593-4](https://doi.org/10.1007/s11356-020-09593-4). google scholar Nguyen, X. P., Hoang, A. T., Ölçer, A. I., & Huynh, T. T. (2021). Record decline in global CO2 emissions prompted by COVID-19 pandemic and its implications on future climate change policies. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 1-4. [https://doi.org/10.1080/15567036.2021.1879969](https://doi.org/10.1080/15567036.2021.1879969). google scholar Pagnottoni P. (2023). Superhighways and roads of multivariate time series shock transmission: Application to cryptocurrency, carbon emission and energy prices. Physica A, 615 (128581). [https://doi.org/10.1016/j.physa.2023.128581](https://doi.org/10.1016/j.physa.2023.128581). google scholar Pea-Assounga, J. B. B., & Wu, M. (2022). Impact of financial development and renewable energy consumption on environmental sustainability: a spatial analysis in CEMAC countries. Environmental Science and Pollution Research, 29, 58341-58359. [https://doi.org/10.1007/s11356-022-19972-8](https://doi.org/10.1007/s11356-022-19972-8). google scholar Peng, G., Meng, F., Ahmed, Z., Ahmad, M., & Kurbonov, K. (2022). Economic growth, technology, and CO2 emissions in BRICS: Investigating the non-linear impacts of economic complexity. Environmental Science and Pollution Research, 29, 68051-68062. [https://doi.org/10.1007/s11356-022-20647-7](https://doi.org/10.1007/s11356-022-20647-7). google scholar Peters, G. P., Marland, G., Quéré, C. Le, Boden, T., Canadell, J. G., & Raupach, M. R. (2012). Rapid growth in CO2 emissions after the 2008-2009 global financial crisis. In Nature Climate Change, 2, 2-4. [https://doi.org/10.1038/nclimate1332](https://doi.org/10.1038/nclimate1332). google scholar Quéré, C. Le, Peters, G. P., Friedlingstein, P., Andrew, R. M., Canadell, J. G., Davis, S. J., Jackson, R. B., & Jones, M. W. (2021). Fossil CO2 emissions in the post-COVID-19 era. Nature Climate Change, 11, 197-199. [https://doi.org/10.1038/s41558-021-01001-0](https://doi.org/10.1038/s41558-021-01001-0). google scholar Qunfang, G., & Huang, X. (2023). The influence of energy consumption and research and development on carbon emission in China: a modified spatial Durbin model approach. Environmental Science and Pollution Research, 30, 44173-44186. [https://doi.org/10.1007/s11356-023-25429-3](https://doi.org/10.1007/s11356-023-25429-3). google scholar Radmehr, R., Henneberry, S. R., & Shayanmehr, S. (2021). Renewable energy consumption, CO2 emissions, and economic growth nexus: A simultaneity spatial modeling analysis of EU countries. Structural Change and Economic Dynamics, 57, 13-27. [https://doi.org/10.1016/j.strueco.2021.01.006](https://doi.org/10.1016/j.strueco.2021.01.006). google scholar Refinitiv Eikon Datastream. 2023. [Retrieved 2023-08-23] Available at: [https://www.refinitiv.com/en/products/datastream-macroeconomic-analysis](https://www.refinitiv.com/en/products/datastream-macroeconomic-analysis). google scholar Ren, X., Cheng, C., Wang, Z., & Yan, C. (2020). Spillover and dynamic effects of energy transition and economic growth on carbon dioxide emissions for the European Union: A dynamic spatial panel model. Sustainable Development, 1-15. [https://doi.org/10.1002/sd.2144](https://doi.org/10.1002/sd.2144). google scholar Shahnazi, R., & Shabani, Z. D. (2021). The effects of renewable energy, spatial spillover of CO2 emissions and economic freedom on CO2 emissions in the EU. Renewable Energy, 169, 293-307. [https://doi.org/10.1016/j.renene.2021.01.016](https://doi.org/10.1016/j.renene.2021.01.016). google scholar Shirazi, M. & Šimurina, J. (2022). Dynamic behavioral characteristics of carbon dioxide emissions from energy consumption: the role of shale technology. Environmental Science and Pollution Research, 29, 28829-28853. [https://doi.org/10.1007/s11356-021-18352-y](https://doi.org/10.1007/s11356-021-18352-y). google scholar Tawfeeq, M. (2023). The spillover impacts of urbanization and energy usage on CO2 emissions: A regional analysis in the United States. Energy Exploration & Exploitation, 0(0), 1-16. [https://doi.org/10.1177/01445987231158145](https://doi.org/10.1177/01445987231158145). google scholar UNDP. (2023). Sustainable Development Goals. [Retrieved 2023-07-15] Available at: [https://www.undp.org/sustainable-development-goals](https://www.undp.org/sustainable-development-goals). google scholar United Nations. (2021). Climate change ‘biggest threat modern humans have ever faced’, world-renowned naturalist tells security council, calls for greater global cooperation. [Retrieved 2023-09-15] Available at: [https://press.un.org/en/2021/sc14445.doc.htm](https://press.un.org/en/2021/sc14445.doc.htm). google scholar United Nations. (2023). For a livable climate: Net-zero commitments must be backed by credible action. [Retrieved 2023-09-12] Available at:. [https://www.un.org/en/climatechange/net-zero-coalition](https://www.un.org/en/climatechange/net-zero-coalition). google scholar Vo, D. H. & Vo, L. H. (2022). International volatility transmission among income, CO2 emission, non-renewable and renewable energy consumption: Which causes which and when? Energy Reports, 8: 10061-10071. [https://doi.org/10.1016/j.egyr.2022.07.168](https://doi.org/10.1016/j.egyr.2022.07.168). google scholar Wen, Q., Chen, Y., Hong, J., Chen, Y., Ni, D., & Shen, Q. (2020). Spillover effect of technological innovation on CO2 emissions in China’s construction industry. Building and Environment, 171 (106653). [https://doi.org/10.1016//j.buildenv.2020.106653](https://doi.org/10.1016//j.buildenv.2020.106653). google scholar Wu, J., Abban, O. J., Boadi, A. D., & Charles, O. (2022). The effects of energy price, spatial spillover of CO2 emissions, and economic freedom on CO2 emissions in Europe: a spatial econometrics approach. Environmental Science and Pollution Research, 29, 63782-63798. [https://doi.org/10.1007/s11356-022-20179-0](https://doi.org/10.1007/s11356-022-20179-0). google scholar You, W., & Lv, Z. (2018). Spillover effects of economic globalization on CO2 emissions: A spatial panel approach. Energy Economics, 73, 248-257. [https://doi.org/10.1016/j.eneco.2018.05.016](https://doi.org/10.1016/j.eneco.2018.05.016). google scholar Zhang, D., Chen, X. H., Lau, C. K. M., & Xu, B. (2023). Implications of cryptocurrency energy usage on climate change. Technological Forecasting & Social Change, 187 (122219). [https://doi.org/10.1016/j.techfore.2022.122219](https://doi.org/10.1016/j.techfore.2022.122219). google scholar Zhang, G., Zhang, N., & Liao, W. (2018). How do population and land urbanization affect CO2 emissions under gravity center change? A spatial econometric analysis” Journal of Cleaner Production, 202, 510-523. [https://doi.org/10.1016/j.jclepro.2018.08.146](https://doi.org/10.1016/j.jclepro.2018.08.146). google scholar Zhang, Q., Yang, J., Sun, Z., & Wu, F. (2017). Analyzing the impact factors of energy-related CO2 emissions in China: What can spatial panel regressions tell us? Journal of Cleaner Production, 161, 1085-1093. [https://doi.org/10.1016/j.jclepro.2017.05.071](https://doi.org/10.1016/j.jclepro.2017.05.071). google scholar