The criticality of clean energy and ICT invesment in achieving environmental sustainability in the EU member states
DOI:
https://doi.org/10.12928/optimum.v15i2.12981Keywords:
Clean energy, Environmental sustainability, ICT invesmentAbstract
Defining and designing appropriate energy, economic and environmental policies that help minimize global carbon emissions remains a top priority for all governmental and non-governmental environmental organizations worldwide. In this digital age, the researcher has paid particular attention to his increasing use of ICT and its relevance to economic and environmental aspects. This paper addresses the sustainability challenges and energy security issues posed by rising energy demand, researchers and policymakers have identified clean future energy alternatives using the most recent data to provide important information for policymakers. The study focused on the key components of ICT investments to promote clean energy (renewables and nuclear) and carbon neutrality in a particular economy with the use of the most robust econometric panel data method for the latest available data sets to obtain reliable and efficient estimates. The study findings demonstrate that using renewable energy can help the EU achieve energy security while reducing greenhouse gas emissions. However, renewable energy deployment is still not substantial enough to mitigate environmental pollution in the presence of significant ICT investment in the EU member states.
References
Adams, S., & Klobodu, E. K. M. (2018). Financial development and environmental degradation: Does political regime matter? Journal of Cleaner Production, 197(1), 1472-1479. https://doi.org/10.1016/j.jclepro.2018.06.252
Ahmed, M. M., & Shimada, K. (2019). The effect of renewable energy consumption on sustainable economic development: Evidence from emerging and developing economies. Energies, 12(15). https://doi.org/10.3390/en12152954
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29-51. https://doi.org/10.1016/0304-4076(94)01642-D
Avom, D., Nkengfack, H., Fotio, H. K., & Totouom, A. (2020). ICT and environmental quality in Sub-Saharan Africa: Effects and transmission channels. Technological Forecasting and Social Change, 155. https://doi.org/10.1016/j.techfore.2020.120028
Azam, A., Rafiq, M., Shafique, M., Zhang, H., & Yuan, J. (2021). Analyzing the effect of natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-variate panel data analysis. Energy, 219. https://doi.org/10.1016/j.energy.2020.119592
Baek, J. (2015). A panel cointegration analysis of CO2 emissions, nuclear energy and income in major nuclear generating countries. Applied Energy, 145, 133-138. https://doi.org/10.1016/j.apenergy.2015.01.074
Baltagi, B. H. (2005). Econometric analysis of panel data (3rd ed.). John Wiley & Sons, Ltd.
Bello, A. A., Renai, J., Hassan, A., Akadiri, S. S., & Itari, A. R. (2023). Synergy effects of ICT diffusion and foreign direct investment on inclusive growth in Sub-Saharan Africa. Environmental Science and Pollution Research, 30, 9428-9444. https://doi.org/10.1007/s11356-022-22689-3
Ben Jebli, M., & Ben Youssef, S. (2017). Renewable energy, arable land, agriculture, CO2 emissions, and economic growth in Morocco (MPRA Paper 76798).
Benzie, M., & Persson, A. (2019). Governing borderless climate risks: moving beyond the territorial framing of adaptation. International Environmental Agreements: Politics, Law and Economics, 19, 369-393. https://doi.org/10.1007/s10784-019-09441-y
Bildirici, M. E., Castanho, R. A., Kayıkçı, F., & Yılmaz Genç, S. (2022). ICT, Energy Intensity, and CO2 Emission Nexus. Energies, 15(13). https://doi.org/10.3390/en15134567
Borhan, H., Vahidi, A., Phillips, A. M., Kuang, M. L., Kolmanovsky, I. V, & Di Cairano, S. (2012). MPC-based energy management of a power-split hybrid electric vehicle. IEEE Transactions on Control Systems Technology, 20(3), 593-603. https://doi.org/10.1109/TCST.2011.2134852
Calado, B., Luke, T. ., Connolly, D. A., Landström, S., & Otgaar, H. (2021). Implanting false autobiographical memories for repeated events. Memory, 29(10), 1320-1341. https://doi.org/10.1080/09658211.2021.1981944
Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B., Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillet, N. P., Houghton, R. A., & Marland, G. (2007). Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Sustainability Science, 104(47), 18866-18870. https://doi.org/10.1073/pnas.0702737104
Charfeddine, L., & Khediri, K. B. (2016). Financial development and environmental quality in UAE: Cointegration with structural breaks. Renewable and Sustainable Energy Reviews, 55, 1322-1335. https://doi.org/10.1016/j.rser.2015.07.059
Chen, R., & Majeed, M. T. (2024). How does green investment respond to ICT and financial development? Borsa Istanbul Review, 24(6), 1067-1076. https://doi.org/10.1016/j.bir.2024.06.003
Chen, X., Gong, X., Li, D., & Zhang, J. (2019). Can information and communication technology reduce CO2 emission? A quantile regression analysis. Environmental Science and Pollution Research, 26(32). https://doi.org/10.1007/s11356-019-06380-8
Dong, K., Sun, R., Jiang, H., & Zeng, X. (2018). CO2 emissions, economic growth, and the environmental Kuznets curve in China: What roles can nuclear energy and renewable energy play? Journal of Cleaner Production, 196. https://doi.org/10.1016/j.jclepro.2018.05.271
EIA. (2013). Aeo2013 (Annual Energy Outlook 2013).
Faisal, Azizullah, Tursoy, T., & Pervaiz, R. (2020). Does ICT lessen CO2 emissions for fast-emerging economies? An application of the heterogeneous panel estimations. Environmental Science and Pollution Research, 27, 10778-10789. https://doi.org/10.1007/s11356-019-07582-w
Godil, D. I., Sharif, A., Agha, H., & Jermsittiparsert, K. (2020). The dynamic nonlinear influence of ICT, financial development, and institutional quality on CO2 emission in Pakistan: new insights from QARDL approach. Environmental Science and Pollution Research, 27(19). https://doi.org/10.1007/s11356-020-08619-1
Gürlek, M., & Tuna, M. (2018). Reinforcing competitive advantage through green organizational culture and green innovation. Service Industries Journal, 38(7-8). https://doi.org/10.1080/02642069.2017.1402889
Higón, D. A., Gholami, R., & Shirazi, F. (2017). ICT and environmental sustainability: A global perspective. Telematics and Informatics, 34(4), 85-95. https://doi.org/10.1016/j.tele.2017.01.001
Hilty, L. M., Arnfalk, P., Erdmann, L., Goodman, J., Lehmann, M., & Wäger, P. A. (2006). The relevance of information and communication technologies for environmental sustainability - A prospective simulation study. Environmental Modelling & Software, 21(11), 1618-1629. https://doi.org/10.1016/j.envsoft.2006.05.007
Huang, Z., Yuan, X., Liu, X., & Tang, Q. (2023). Growing control of climate change on water scarcity alleviation over northern part of China. Journal of Hydrology: Regional Studies, 46. https://doi.org/10.1016/j.ejrh.2023.101332
Ibn-Mohammed, T., Mustapha, K. B., Godsell, J., Adamu, Z., Babatunde, K. A., Akintade, D. D., Acquaye, A., Fujii, H., Ndiaye, M. M., Yamoah, F. A., & Koh, S. C. L. (2021). A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies. Resources, Conservation and Recycling, 164. https://doi.org/10.1016/j.resconrec.2020.105169
Jaunky, V. C. (2011). The CO2 emissions-income nexus: Evidence from rich countries. Energy Policy, 39(3), 1228-1240. https://doi.org/10.1016/j.enpol.2010.11.050
Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1-44. https://doi.org/10.1016/S0304-4076(98)00023-2
Kapoor, K., Bigdeli, A. Z., Dwivedi, Y. K., Schroeder, A., Beltagui, A., & Baines, T. (2021). A socio-technical view of platform ecosystems: Systematic review and research agenda. Journal of Business Research, 128, 94-108. https://doi.org/10.1016/j.jbusres.2021.01.060
Kelly, T., & Adolph, M. (2008). ITU-T initiatives on climate change. IEEE Communications Magazine, 46(10), 108-114. https://doi.org/10.1109/MCOM.2008.4644127
Khan, H., Khan, I., & Binh, T. T. (2020). The heterogeneity of renewable energy consumption, carbon emission and financial development in the globe: A panel quantile regression approach. Energy Reports, 6. https://doi.org/10.1016/j.egyr.2020.04.002
Khan, H., Weili, L., & Khan, I. (2022). Examining the effect of information and communication technology, innovations, and renewable energy consumption on CO2 emission: evidence from BRICS countries. Environmental Science and Pollution Research, 29, 47696-47712. https://doi.org/10.1007/s11356-022-19283-y
Koondhar, M. A., Tan, Z., Alam, G. M., Khan, Z. A., Wang, L., & Kong, R. (2021). Bioenergy consumption, carbon emissions, and agricultural bioeconomic growth: A systematic approach to carbon neutrality in China. Journal of Environmental Management, 296. https://doi.org/10.1016/j.jenvman.2021.113242
Le Quéré, C., Peters, G. P., Andres, R. J., Andrew, R. M., Boden, T. A., Ciais, P., Friedlingstein, P., Houghton, R. A., Marland, G., Moriarty, R., Sitch, S., Tans, P., Arneth, A., Arvanitis, A., Bakker, D. C. E., Bopp, L., Canadell, J. G., Chini, L. P., Doney, S. C., … Zaehle, S. (2014). Global carbon budget 2013. Earth System Science Data, 6(1), 235-263. https://doi.org/10.5194/essd-6-235-2014
Lee, J. W., & Brahmasrene, T. (2014). ICT, CO2 Emissions and Economic Growth: Evidence from a Panel of ASEAN. Global Economic Review, 43(2), 93-109. https://doi.org/10.1080/1226508X.2014.917803
Leszczensky, L., & Wolbring, T. (2019). How to deal with reverse causality using panel data? Recommendations for researchers based on a simulation study. Sociological Methods & Research, 51(2). https://doi.org/10.1177/0049124119882473
Lu, W.-C. (2018). The impacts of information and communication technology, energy consumption, financial development, and economic growth on carbon dioxide emissions in 12 Asian countries. Mitigation and Adaptation Strategies for Global Change, 23, 1351-1365. https://doi.org/10.1007/s11027-018-9787-y
Mbow, H.-O. P., Reisinger, A., Canadell, J., & O'Brien, P. (2017). Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SR2). Ginevra, IPCC, 650.
Mikayilov, J. I., Hasanov, F. J., & Galeotti, M. (2018). Decoupling of CO2 emissions and GDP: A time-varying cointegration approach. Ecological Indicators, 95(1), 615-628. https://doi.org/10.1016/j.ecolind.2018.07.051
Moyer, J. D., & Hughes, B. B. (2012). ICTs: Do they contribute to increased carbon emissions? Technological Forecasting and Social Change, 79(5), 919-931. https://doi.org/10.1016/j.techfore.2011.12.005
Naminse, E. Y., & Zhuang, J. (2018). Does farmer entrepreneurship alleviate rural poverty in China? Evidence from Guangxi Province. PLoS ONE, 13(3). https://doi.org/10.1371/journal.pone.0194912
Ollo-López, A., & Aramendía-Muneta, M. E. (2012). ICT impact on competitiveness, innovation and environment. Telematics and Informatics, 29(2), 204-210. https://doi.org/10.1016/j.tele.2011.08.002
Park, Y., Meng, F., & Baloch, M. A. (2018). The effect of ICT, financial development, growth, and trade openness on CO2 emissions: an empirical analysis. Environmental Science and Pollution Research, 25, 30708-30719. https://doi.org/10.1007/s11356-018-3108-6
Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels. In Advances in Econometrics Nonstationary Panels, Panel Cointegration, and Dynamic Panels. Emerald Group Publishing Limited.
Pesaran, M. H. (2015). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10). https://doi.org/10.1080/07474938.2014.956623
Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50-93. https://doi.org/10.1016/j.jeconom.2007.05.010
Prakosa, B. G., Guritno, D. C., Anindita, T., Kurniawan, M., & Nugroho, A. C. (2024). Correlation among components of the Indonesian industry readiness index 4.0 and its implementation on socioeconomic along with the demographic aspects. Digital Transformation and Society, 3(3), 296-309. https://doi.org/10.1108/DTS-08-2023-0063
Rogelj, J., & Schleussner, C.-F. (2019). Unintentional unfairness when applying new greenhouse gas emissions metrics at country level. Environmental Research Letters, 14(11). https://doi.org/10.1088/1748-9326/ab4928
Saidi, K., & Omri, A. (2020). Reducing CO2 emissions in OECD countries: Do renewable and nuclear energy matter? Progress in Nuclear Energy, 126. https://doi.org/10.1016/j.pnucene.2020.103425
Sharma, G. D., Rahman, M. M., Jain, M., & Chopra, R. (2021). Nexus between energy consumption, information and communications technology, and economic growth: An enquiry into emerging Asian countries. Journal of Public Affairs, 21(2). https://doi.org/10.1002/pa.2172
Soto, G. H., & Martinez-Cobas, X. (2024). Nuclear energy generation's impact on the CO2 emissions and ecological footprint among European Union countries. Science of the Total Environment, 945. https://doi.org/10.1016/j.scitotenv.2024.173844
Terhaar, J., Fröliche, T. L., Aschwanden, M. T., Friedlingstein, P., & Joos, F. (2022). Adaptive emission reduction approach to reach any global warming target. Nature Climate Change, 12, 1136-1142. https://doi.org/10.1038/s41558-022-01537-9
UNCTAD. (2023). World Invesment Report 2022.
Usman, O., Alola, A. A., & Akadiri, S. S. (2022). Effects of domestic material consumption, renewable energy, and financial development on environmental sustainability in the EU-28: Evidence from a GMM panel-VAR. Renewable Energy, 184, 239-251. https://doi.org/10.1016/j.renene.2021.11.086
Wang, M., Yao, M., Wang, S., Qian, H., Zhang, P., Wang, Y., Sun, Y., & Wei, W. (2021). Study of the emissions and spatial distributions of various power-generation technologies in China. Journal of Environmental Management, 278. https://doi.org/10.1016/j.jenvman.2020.111401
Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 709-748. https://doi.org/10.1111/j.1468-0084.2007.00477.x
Westerlund, J., & Hosseinkouchack, M. (2016). Modified CADF and CIPS panel unit root statistics with standard chi-squared and normal limiting distributions. Oxford Bulletin of Economics and Statistics, 78(3), 347-364. https://doi.org/10.1111/obes.12127
Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). The MIT Press.
Yoshino, T., Suzuki, T., Nagamatsu, G., Yabukami, H., Ikegaya, M., Kishima, M., Kita, H., Imamura, T., Nakashima, K., Nishinakamura, R., Tachibana, M., Inoue, M., Shima, Y., Morohashi, K. I., & Hayashi, K. (2021). Generation of ovarian follicles from mouse pluripotent stem cells. Science, 16(373). https://doi.org/10.1126/science.abe0237
Zhang, C., & Liu, C. (2015). The impact of ICT industry on CO2 emissions: A regional analysis in China. Renewable and Sustainable Energy Reviews, 44, 12-19. https://doi.org/10.1016/j.rser.2014.12.011
Zhang, J., Yu, S., Xiong, X., & Hu, X. (2024). Impacts of ICT penetration shaping nonworking time use on indirect carbon emissions: Evidence from Chinese households. Energy Economics, 129. https://doi.org/10.1016/j.eneco.2023.107190
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Hassan Swedy Lunku

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.






