Heterogeneity in Air Pollution Levels and Their Techno‑economic Determinants: A Cluster Analysis of the EU–27
DOI:
https://doi.org/10.18778/1508-2008.27.21Keywords:
air pollution, greenhouse gas (GHG) emissions, acidifying gas (ACG) emissions, cluster analysis, European Union countriesAbstract
The ongoing decline in environmental quality is one of the biggest global challenges facing humankind today. The purpose of this study is to investigate the differences and similarities among the EU–27 countries regarding air pollution emissions (greenhouse gases and acidifying gases) and their techno-economic determinants, which encompass economic, energy, innovation and institutional quality factors. The analysis covers nine indicators that reflect pollution emissions and fifteen variables that illustrate air pollution drivers. Cluster analysis of the data averaged for the period 2015–2020 was used to identify subgroups of countries. The results show that European Union (EU) countries substantially differ in terms of both air pollution levels and the determinants of the emissions. The analysis revealed a noticeable division between Eastern EU countries, which show similar patterns both in terms of pollution and determinants, and Western EU countries, which were characterised by greater diversity in terms of the analysed features. In light of the results, the assertion about backward and polluted new EU member states compared to more advanced and environmentally uncontaminated old EU countries appears to oversimplify the reality. The findings contribute to the ongoing discussion on environmental quality. Our results indicate the need and space for initiatives that address factors that influence air pollution in order to impede environmental degradation. However, due to the revealed heterogeneity among countries, the efforts should be tailored to the specific country’s characteristics.
Downloads
References
Aghel, B., Sahraie, S., Heidaryan, E. (2020), Carbon dioxide desorption from aqueous solutions of monoethanolamine and diethanolamine in a microchannel reactor, “Separation and Purification Technology”, 237, 116390, https://doi.org/10.1016/j.seppur.2019.116390
Google Scholar
Arminen, H., Menegaki, A.N. (2019), Corruption, climate and the energy-environment growth nexus, “Energy Economics”, 80, pp. 621–634, https://doi.org/10.1016/j.eneco.2019.02.009
Google Scholar
Aung, T.S., Fischer, T.B., Azmi, A.S. (2020), Are large-scale dams environmentally detrimental? Life-cycle environmental consequences of mega-hydropower plants in Myanmar, “The International Journal of Life Cycle Assessment”, 25, pp. 1749–1766, https://doi.org/10.1007/s11367-020-01795-9
Google Scholar
Bai, C., Feng, C., Yan, H., Yi, X., Chen, Z., Wei, W. (2020), Will income inequality influence the abatement effect of renewable energy technological innovation on carbon dioxide emissions?, “Journal of Environmental Management”, 264, 110482, https://doi.org/10.1016/j.jenvman.2020.110482
Google Scholar
Bekun, F.V., Gyamfi, B.A., Onifade, S.T., Agboola, M.O. (2021), Beyond the environmental Kuznets Curve in E7 economies: accounting for the combined impacts of institutional quality and renewables, “Journal of Cleaner Production”, 314, 127924, https://doi.org/10.1016/j.jclepro.2021.127924
Google Scholar
Cheng, C., Ren, X., Wang, Z., Yan, C. (2019), Heterogeneous impacts of renewable energy and environmental patents on CO2 emission – Evidence from the BRIICS, “Science of the Total Environment”, 668, pp. 1328–1338, https://doi.org/10.1016/j.scitotenv.2019.02.063
Google Scholar
Cheng, C., Ren, X., Dong, K., Dong, X., Wang, Z. (2021), How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression, “Journal of Environmental Management”, 280, 111818, https://doi.org/10.1016/j.jenvman.2020.111818
Google Scholar
Chien, F., Anwar, A., Hsu, C.-C., Sharif, A., Razzaq, A., Sinha, A. (2021), The role of information and communication technology in encountering environmental degradation: Proposing an SDG framework for the BRICS countries, “Technology in Society”, 65, 101587, https://doi.org/10.1016/j.techsoc.2021.101587
Google Scholar
Cifuentes-Faura, J. (2022), European Union policies and their role in combating climate change over the years, “Air Quality, Atmosphere & Health”, 15, pp. 1333–1340, https://doi.org/10.1007/s11869-022-01156-5
Google Scholar
Consolidated versions of the Treaty on European Union and the Treaty on the functioning of the European Union (2012/c 326/01), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:12012E/TXT (accessed: 28.04.2023).
Google Scholar
Du, K., Li, P., Yan, Z. (2019), Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data, “Technological Forecasting and Social Change”, 146, pp. 297–303, https://doi.org/10.1016/j.techfore.2019.06.010
Google Scholar
Ehigiamusoe, K.U., Lean, H.H., Smyth, R. (2020), The moderating role of energy consumption in the carbon emissions-income nexus in middle-income countries, “Applied Energy”, 261, 114215, https://doi.org/10.1016/j.apenergy.2019.114215
Google Scholar
European Environment Agency (2023), Europe’s air quality status 2023, https://www.eea.europa.eu/publications/europes-air-quality-status-2023 (accessed: 28.04.2023).
Google Scholar
European Parliament (2018), Climate change in Europe: facts and figures, https://www.europarl.europa.eu/news/en/headlines/priorities/climate-change/20180703STO07123/climate-change-in-europe-facts-and-figures (accessed: 26.04.2023).
Google Scholar
European Parliament (2023), Combating climate change, https://www.europarl.europa.eu/factsheets/en/sheet/72/combating-climate-change (accessed: 26.04.2023).
Google Scholar
Eurostat (2023), Key figures on the EU in the world. 2023 edition, https://doi.org/10.2785/515035
Google Scholar
Gholipour, H.F., Farzanegan, M.R. (2018), Institutions and the effectiveness of expenditures on environmental protection: evidence from Middle Eastern countries, “Constitutional Political Economy”, 29 (1), pp. 20–39, https://doi.org/10.1007/s10602-017-9246-x
Google Scholar
Govender, P., Sivakumar, V. (2020), Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019), “Atmospheric Pollution Research”, 11 (1), pp. 40–56, https://doi.org/10.1016/j.apr.2019.09.009
Google Scholar
Guterres, I. (2022), Enforcing Environmental Policy – the role of the European Union, “UNIO – EU Law Journal”, 8 (1), pp. 32–52, https://doi.org/10.21814/unio.8.1.4522
Google Scholar
Hall, B.H. (2007), Measuring the returns to R&D: The depreciation problem, “NBER Working Paper”, 13473, https://doi.org/10.3386/w13473
Google Scholar
Hastie, T., Tibshirani, R., Friedman, J.H. (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, https://doi.org/10.1007/978-0-387-84858-7
Google Scholar
Işık, C., Ongan, S., Özdemir, D. (2019), Testing the EKC hypothesis for ten US states: an application of heterogeneous panel estimation method, “Environmental Science and Pollution Research”, 26, pp. 10846–10853, https://doi.org/10.1007/s11356-019-04514-6
Google Scholar
Jajuga, K., Walesiak, M. (2000), Standardisation of Data Set under Different Measurement Scales, [in:] R. Decker, W. Gaul (eds.), Classification and Information Processing at the Turn of the Millennium, Springer-Verlag, Berlin–Heidelberg, pp. 105–112, https://doi.org/10.1007/978-3-642-57280-7_11
Google Scholar
Jinqiao, L., Maneengam, A., Saleem, F., Mukarram, S.S. (2022), Investigating the role of financial development and technology innovation in climate change: evidence from emerging seven countries, “Economic Research – Ekonomska Istraživanja”, 35 (1), pp. 3940–3960, https://doi.org/10.1080/1331677X.2021.2007152
Google Scholar
Karim, S., Appiah, M., Naeem, M.A., Lucey, B.M., Li, M. (2022), Modelling the role of institutional quality on carbon emissions in Sub-Saharan African countries, “Renewable Energy”, 198, pp. 213–221, https://doi.org/10.1016/j.renene.2022.08.074
Google Scholar
Kaufmann, D., Kraay, A. (2023), Worldwide Governance Indicators, 2023 Update, https://www.govindicators.org (accessed: 27.10.2023).
Google Scholar
Kaufmann, D., Kraay, A., Mastruzzi, M. (2010), The Worldwide Governance Indicators: Methodology and Analytical Issues, “World Bank Policy Research Working Paper”, 5430, https://ssrn.com/abstract=1682130 (accessed: 27.10.2023).
Google Scholar
Khan, H., Weili, L., Khan, I. (2022), Institutional quality, financial development and the influence of environmental factors on carbon emissions: evidence from a global perspective, “Environmental Science and Pollution Research”, 29 (9), pp. 13356–13368, https://doi.org/10.1007/s11356-021-16626-z
Google Scholar
Kula, F., Ünlü, F. (2019), Ecological Innovation Efforts and Performances: An Empirical Analysis, [in]: M. Shahbaz, D. Balsalobre (eds.), Energy and Environmental Strategies in the Era of Globalization, Springer, Cham, pp. 221–250, https://doi.org/10.1007/978-3-030-06001-5_9
Google Scholar
Lingyan, M., Zhao, Z., Malik, H.A., Razzaq, A., An, H., Hassan, M. (2022), Asymmetric impact of fiscal decentralization and environmental innovation on carbon emissions: Evidence from highly decentralized countries, “Energy & Environment”, 33 (4), pp. 752–782, https://doi.org/10.1177/0958305X211018453
Google Scholar
Liu, X., Bae, J. (2018), Urbanization and industrialization impact of CO2 emissions in China, “Journal of Cleaner Production”, 172, pp. 178–186, https://doi.org/10.1016/j.jclepro.2017.10.156
Google Scholar
Mehmood, U., Tariq, S., Ul-Haq, Z., Meo, M.S. (2021), Does the modifying role of institutional quality remains homogeneous in GDP-CO2 emission nexus? New evidence from ARDL approach, “Environmental Science and Pollution Research”, 28, pp. 10167–10174, https://doi.org/10.1007/s11356-020-11293-y
Google Scholar
Nielsen, F. (2016), Introduction to HPC with MPI for Data Science, Springer, Cham, https://doi.org/10.1007/978-3-319-21903-5
Google Scholar
OECD (2008), Handbook on Constructing Composite Indicators: Methodology and User Guide, OECD Publishing, Paris, https://doi.org/10.1787/9789264043466-en
Google Scholar
Ongan, S., Isik, C., Ozdemir, D. (2020), Economic growth and environmental degradation: evidence from the US case environmental Kuznets curve hypothesis with application of decomposition, “Journal of Environmental Economics and Policy”, 10 (1), pp. 14–21, https://doi.org/10.1080/21606544.2020.1756419
Google Scholar
Piva, M., Vivarelli, M. (2018), Technological change and employment: is Europe ready for the challenge?, “Eurasian Business Review”, 8 (1), pp. 13–32, https://doi.org/10.1007/s40821-017-0100-x
Google Scholar
Regulation (EU) 2021/1119 of the European Parliament and of the Council of 30 June 2021 establishing the framework for achieving climate neutrality and amending Regulations (EC) No. 401/2009 and (EU) 2018/1999 (‘European Climate Law’), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32021R1119 (accessed: 27.04.2023).
Google Scholar
Shan, S., Genç, S.Y., Kamran, H.W., Dinca, G. (2021), Role of green technology innovation and renewable energy in carbon neutrality: A sustainable investigation from Turkey, “Journal of Environmental Management”, 294, 113004, https://doi.org/10.1016/j.jenvman.2021.113004
Google Scholar
Singh, A., Agrawal, M. (2008), Acid rain and its ecological consequences, “Journal of Environmental Biology”, 29 (1), pp. 15–24.
Google Scholar
Wang, Q., Yang, T., Li, R. (2023), Does income inequality reshape the environmental Kuznets curve (EKC) hypothesis? A nonlinear panel data analysis, “Environmental Research”, 216, 114575, https://doi.org/10.1016/j.envres.2022.114575
Google Scholar
Wang, S., Zeng, J., Liu, X. (2019), Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach, “Renewable and Sustainable Energy Reviews”, 103, pp. 140–150, https://doi.org/10.1016/j.rser.2018.12.046
Google Scholar
Wawrzyniak, D., Doryń, W. (2020), Does the quality of institutions modify the economic growth-carbon dioxide emissions nexus? Evidence from a group of emerging and developing countries, “Economic Research – Ekonomska Istraživanja”, 33 (1), pp. 124–144, https://doi.org/10.1080/1331677X.2019.1708770
Google Scholar
Weina, D., Gilli, M., Mazzanti, M., Nicolli, F. (2016), Green inventions and greenhouse gas emission dynamics: a close examination of provincial Italian data, “Environmental Economics and Policy Studies”, 18 (2), pp. 247–263, https://doi.org/10.1007/s10018-015-0126-1
Google Scholar
World Health Organization (2021), WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, Geneva, https://apps.who.int/iris/handle/10665/345329 (accessed: 27.10.2023).
Google Scholar
Wu, W.L. (2017), Institutional Quality and Air Pollution: International Evidence, “International Journal of Business and Economics”, 16 (1), pp. 49–74, https://ijbe.fcu.edu.tw/past_issues/NO.16-1/pdf/vol_16-1-4.pdf (accessed: 18.04.2023).
Google Scholar
Yildirim, J., Alpaslan, B., Eker, E.E. (2021), The role of social capital in environmental protection efforts: Evidence from Turkey, “Journal of Applied Statistics”, 48 (13–15), pp. 2626–2642, https://doi.org/10.1080/02664763.2020.1843609
Google Scholar
Zhang, Y.-J., Liu, Z., Zhang, H., Tan, T.-D. (2014), The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China, “Natural Hazards”, 73, pp. 579–595, https://doi.org/10.1007/s11069-014-1091-x
Google Scholar
Zheng, Y.M., Lv, Q., Wang, Y.D. (2022), Economic development, technological progress, and provincial carbon emissions intensity: empirical research based on the threshold panel model, “Applied Economics”, 54 (30), pp. 3495–3504, https://doi.org/10.1080/00036846.2021.2009760
Google Scholar
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.