Technological innovation and energy transition in G7 economies: Country-specific evidence from quantile and wavelet approaches

Öz

The aim of this study is to examine the impact of technological innovation on renewable energy consumption in the G7 countries. Annual data covering the period 1985-2021 are used, and the analysis focuses on the G7 countries, including Canada, Germany, France, Italy, Japan, the United Kingdom, and the United States. Renewable energy consumption is considered the dependent variable, while technological innovation is represented by patent applications. In order to analyse the relationship between the variables across different distribution levels and time scales more comprehensively, the Modified Cross Quantile Regression (MCQR), Quantile-on-Quantile Kernel Regularized Least Squares (QQKRLS), and Wavelet Quantile Granger Causality (WQGC) methods are employed. The empirical findings reveal the existence of a nonlinear and quantile-dependent relationship between technological innovation and renewable energy consumption. The results also indicate that the impact of technological innovation on renewable energy consumption exhibits a heterogeneous structure across countries and varies across different quantiles. In addition, the findings identify the presence of time- and quantile-dependent causal relationships running from technological innovation to renewable energy consumption. The findings of this study provide important implications for policymakers, energy sector stakeholders, and researchers by improving the understanding of the role of technological innovation in shaping energy transition policies.

Anahtar Kelimeler

• Renewable energy consumption
• Technological innovation
• Energy transition
• G7 countries

Kaynakça

1. Adebayo, T. S., Özkan, O., & Eweade, B. S. (2024a). Do energy efficiency R&D investments and information and communication technologies promote environmental sustainability in Sweden? A quantile-on-quantile KRLS investigation. Journal of Cleaner Production, 440, 140832. https://doi.org/10.1016/j.jclepro.2024.140832
2. Adebayo, T. S., Meo, M. S., Eweade, B. S., & Özkan, O. (2024b). Examining the effects of solar energy innovations, information and communication technology and financial globalization on environmental quality in the United States via quantile-on-quantile KRLS analysis. Solar Energy, 272, 112450. https://doi.org/10.1016/j.solener.2024.112450
3. Al-Awamleh, H. K., Omoush, M. M., Ahmed, R. T., Assaf, N., Alqudah, M. Z., & Samara, H. (2026). Innovation in energy management: Mapping knowledge development and technological change. International Journal of Energy Sector Management, 20(3), 734-758. https://doi.org/10.1108/IJESM-03-2025-0016
4. Athari, S. A., Kirikkaleli, D., Saliba, C., & Olanrewaju, V. O. (2025). Unlocking the investment nexus between artificial intelligence and Bitcoin: Modified cross-quantile regression insights. Social Sciences & Humanities Open, 12, 102131. https://dx.doi.org/10.2139/ssrn.5628793
5. Chen, M., Sinha, A., Hu, K., & Shah, M. I. (2021). Impact of technological innovation on energy efficiency in industry 4.0 era: Moderation of shadow economy in sustainable development. Technological Forecasting and Social Change, 164, 120521. https://doi.org/10.1016/j.techfore.2020.120521
6. Ferwerda, J., Hainmueller, J., & Hazlett, C. J. (2017). Kernel-based regularized least squares in R (KRLS) and Stata (KRLS). Journal of Statistical Software, 79, 1-26. https://doi.org/10.18637/jss.v079.i03
7. Geng, J. B., & Ji, Q. (2016). Technological innovation and renewable energy development: Evidence based on patent counts. International Journal of Global Environmental Issues, 15(3), 217-234. https://doi.org/10.1504/IJGENVI.2016.076945
8. Hainmueller, J., & Hazlett, C. (2014). Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach. Political Analysis, 22(2), 143-168. https://doi.org/10.1093/pan/mpt019

9. Herring, H., & Roy, R. (2007). Technological innovation, energy efficient design and the rebound effect. Technovation, 27(4), 194-203. https://doi.org/10.1016/j.technovation.2006.11.004
10. Jalal, M. M. (2025). Energy demand, financial access, and urbanization as determinants of the load capacity factor: Fresh evidence from the United States. International Journal of Business and Economic Studies, 7(4), 272-285. https://doi.org/10.54821/uiecd.1830333
11. Kebede, K. Y., & Mitsufuji, T. (2017). Technological innovation system building for diffusion of renewable energy technology: A case of solar PV systems in Ethiopia. Technological Forecasting and Social Change, 114, 242-253. https://doi.org/10.1016/j.techfore.2016.08.018
12. Khan, A., Chenggang, Y., Hussain, J., & Kui, Z. (2021). Impact of technological innovation, financial development and foreign direct investment on renewable energy, non-renewable energy and the environment in Belt & Road Initiative countries. Renewable Energy, 171, 479-491. https://doi.org/10.1016/j.renene.2021.02.075
13. Khan, K., Su, C. W., Rehman, A. U., & Ullah, R. (2022). Is technological innovation a driver of renewable energy?. Technology in Society, 70, 102044. https://doi.org/10.1016/j.techsoc.2022.102044
14. Khan, D., Nouman, M., & Ullah, A. (2023). Assessing the impact of technological innovation on technically derived energy efficiency: A multivariate co-integration analysis of the agricultural sector in South Asia. Environment, Development and Sustainability, 25(4), 3723-3745. https://doi.org/10.1007/s10668-022-02194-w
15. Khan, K., & Su, C. W. (2023). Does technology innovation complement the renewable energy transition?. Environmental Science and Pollution Research, 30(11), 30144-30154. https://doi.org/10.1007/s11356-022-24336-3
16. Lee, C. C., & Wang, C. S. (2022). Financial development, technological innovation and energy security: Evidence from Chinese provincial experience. Energy Economics, 112, 106161. https://doi.org/10.1016/j.eneco.2022.106161
17. Lee, C. C., Yuan, Z., Lee, C. C., & Chang, Y. F. (2022). The impact of renewable energy technology innovation on energy poverty: Does climate risk matter?. Energy Economics, 116, 106427. https://doi.org/10.1016/j.eneco.2022.106427
18. Li, J., Dong, K., & Dong, X. (2022). Green energy as a new determinant of green growth in China: The role of green technological innovation. Energy Economics, 114, 106260. https://doi.org/10.1016/j.eneco.2022.106260
19. Li, S., Cifuentes-Faura, J., Talbi, B., Sadiq, M., Mohammed, K. S., & Bashir, M. F. (2023). Dynamic correlated effects of electricity prices, biomass energy, and technological innovation in Tunisia's energy transition. Utilities Policy, 82, 101521. https://doi.org/10.1016/j.eneco.2022.106260
20. Li, T. H. (2024). Spline autoregression method for estimation of quantile spectrum. (arXiv:2412.17163). https://doi.org/10.48550/arXiv.2412.17163
21. Miao, C., Fang, D., Sun, L., Luo, Q., & Yu, Q. (2018). Driving effect of technology innovation on energy utilization efficiency in strategic emerging industries. Journal of Cleaner Production, 170, 1177-1184. https://doi.org/10.1016/j.jclepro.2017.09.225
22. Nepal, R., Zhao, X., Dong, K., Wang, J., & Sharif, A. (2025). Can artificial intelligence technology innovation boost energy resilience? The role of green finance. Energy Economics, 142, 108159. https://doi.org/10.1016/j.eneco.2024.108159
23. Ozkan, A. (2026). Exploring the drivers of renewable energy consumption: The roles of innovation, financial globalization, and uncertainty in Next-11 countries. Journal of Research in Economics, Politics & Finance, 11(1), 120-139. https://doi.org/10.30784/epfad.1820631
24. Ozkan, O., Obekpa, H. O., & Alola, A. A. (2025). Examining the nexus of energy intensity, renewables, natural resources, and carbon intensity in India. Energy & Environment, 36(1), 168-186. https://doi.org/10.1177/0958305X231169706
25. Pan, X., Uddin, M. K., Han, C., & Pan, X. (2019). Dynamics of financial development, trade openness, technological innovation and energy intensity: Evidence from Bangladesh. Energy, 171, 456-464. https://doi.org/10.1016/j.energy.2018.12.200
26. Saint Akadiri, S., Ozkan, O., & Alola, A. A. (2025). Investigating the determinants of load capacity factor in Nigeria: An asymmetric quantile approach on urbanization, economic growth, FDI, and resource dependency. Resources Policy, 104, 105586. https://doi.org/10.1016/j.resourpol.2025.105586
27. Sarigul, S. S., & Cetin, M. (2025). Renewable energy, fossil fuels, and globalization: Policy lessons from Japan for a sustainable future. Journal of Sustainable Digital Futures, 1(1), 36-45. http://dx.doi.org/10.29228/jsdf.82693
28. Saudi, M. H. M., Sinaga, O., Roespinoedji, D., & Ghani, E. K. (2019). The impact of technological innovation on energy intensity: Evidence from Indonesia. International Journal of Energy Economics and Policy, 9(3), 11. https://doi.org/10.32479/ijeep.7731
29. Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1-8. https://doi.org/10.1016/j.jbankfin.2015.01.013
30. Solarin, S. A., Bello, M. O., & Tiwari, A. K. (2022). The impact of technological innovation on renewable energy production: Accounting for the roles of economic and environmental factors using a method of moments quantile regression. Heliyon, 8(7). https://doi.org/10.1016/j.heliyon.2022.e09913
31. Song, A., Rasool, Z., Nazar, R., & Anser, M. K. (2024). Towards a greener future: How green technology innovation and energy efficiency are transforming sustainability. Energy, 290, 129891. https://doi.org/10.1016/j.energy.2023.129891
32. Wang, H., & Wang, M. (2020). Effects of technological innovation on energy efficiency in China: Evidence from dynamic panel of 284 cities. Science of the Total Environment, 709, 136172. https://doi.org/10.1016/j.scitotenv.2019.136172
33. Wang, M., Li, Y., & Liao, G. (2021). Research on the impact of green technology innovation on energy total factor productivity, based on provincial data of China. Frontiers in Environmental Science, 9, 710931. https://doi.org/10.3389/fenvs.2021.710931
34. Wang, W., Xiao, W., & Bai, C. (2022). Can renewable energy technology innovation alleviate energy poverty? Perspective from the marketization level. Technology in Society, 68, 101933. https://doi.org/10.1016/j.techsoc.2022.101933
35. Wang, K. H., Wen, C. P., Long, H., & Moldovan, N. C. (2024). Towards sustainable development: Exploring the spillover effects of green technology innovation on energy markets and economic cycles. Technological Forecasting and Social Change, 203, 123368. https://doi.org/10.1016/j.techfore.2024.123368
36. Zafar, S. Z., Zhilin, Q., Malik, H., Abu-Rumman, A., Al Shraah, A., Al-Madi, F., & Alfalah, T. F. (2021). Spatial spillover effects of technological innovation on total factor energy efficiency: Taking government environment regulations into account for three continents. Business Process Management Journal, 27(6), 1874-1891. https://doi.org/10.1108/BPMJ-12-2020-0550
37. Zhang, R., & Fu, Y. (2022). Technological progress effects on energy efficiency from the perspective of technological innovation and technology introduction: An empirical study of Guangdong, China. Energy Reports, 8, 425-437. https://doi.org/10.1016/j.egyr.2021.11.282
38. Zhang, Z., Luo, C., Zhang, G., Shu, Y., & Shao, S. (2024). New energy policy and green technology innovation of new energy enterprises: Evidence from China. Energy Economics, 136, 107743. https://doi.org/10.1016/j.eneco.2024.107743