Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Balancing the nuclear equation: Climate policy uncertainty and budgetary dynamics

  • Chang Li (School of International Relations, Guangdong University of Foreign Studies) ;
  • Sajid Ali (School of Economics, Bahauddin Zakariya University) ;
  • Raima Nazar (Department of Economics, The Women University) ;
  • Muhammad Saeed Meo (Sunway Business School, Sunway University)
  • Received : 2023.10.10
  • Accepted : 2024.02.25
  • Published : 2024.07.25
Download PDF
⟨ Previous Next ⟩

Abstract

Amidst the uncertainties of climate policy, investing in nuclear energy technology emerges as a sustainable strategy, fostering innovation in a critical sector, while simultaneously addressing urgent environmental concerns and managing budgetary dynamics. Our investigation inspects the asymmetric influence of climate policy uncertainty on nuclear energy technology in the top 10 nations with the highest nuclear energy R&D budgets (Germany, Japan, China, France, USA, UK, India, South Korea, Russia, and Canada). Previous studies adopted panel data methods to evaluate the linkage between climate policy uncertainty and nuclear energy technology. Nonetheless, these investigations overlooked the variability in this association across various countries. Conversely, this investigation introduces an innovative tool, 'Quantile-on-Quantile' to probe this connection merely for every economy. This methodology concedes for a more accurate evaluation, offering a holistic global perspective and delivering tailored insights for individual countries. The findings uncover that climate policy uncertainty significantly reduces nuclear energy technology budgets across multiple quantiles in most selected economies. Additionally, our results highlight the asymmetries in the correlations between our variables across the nations. These findings stress the need for policymakers to conduct thorough assessments and skillfully manage climate policy uncertainty and nuclear energy budgets.

Keywords

References

  1. A.E. Caglar, Can nuclear energy technology budgets pave the way for a transition toward low-carbon economy: insights from the United Kingdom, Sustain. Dev. 31 (1) (2023) 198-210. https://doi.org/10.1002/sd.2383
  2. H.O. Obekpa, A.A. Alola, Asymmetric response of energy efficiency to research and development spending in renewables and nuclear energy usage in the United States, Prog. Nucl. Energy 156 (2023) 104522.
  3. X. Wang, et al., Economic policy uncertainty and dynamic correlations in energy markets: assessment and solutions, Energy Econ. 117 (2023) 106475.
  4. S.G. Yalew, et al., Impacts of climate change on energy systems in global and regional scenarios, Nat. Energy 5 (10) (2020) 794-802. https://doi.org/10.1038/s41560-020-0664-z
  5. R.J. Zhang, A. Razzaq, Influence of economic policy uncertainty and financial development on renewable energy consumption in the BRICST region, Renew. Energy 201 (2022) 526-533. https://doi.org/10.1016/j.renene.2022.10.107
  6. X.-Y. Peng, et al., How does economic policy uncertainty affect green innovation? Technol. Econ. Dev. Econ. 29 (1) (2023) 114-140. https://doi.org/10.3846/tede.2022.17760
  7. S. Yi, et al., How economic policy uncertainty and financial development contribute to renewable energy consumption? The importance of economic globalization, Renew. Energy 202 (2023) 1357-1367.
  8. O. Ozkan, et al., Investigating the nexus between economic complexity and energyrelated environmental risks in the USA: empirical evidence from a novel multivariate quantile-on-quantile regression, Struct. Change Econ. Dynam. 65 (2023) 382-392.
  9. F. Shahzad, et al., Examining the asymmetric link between clean energy intensity and carbon dioxide emissions: the significance of quantile-on-quantile method, Energy Environ. 34 (6) (2023) 1884-1909. https://doi.org/10.1177/0958305X221102049
  10. A.E. Caglar, M. Ulug, The role of government spending on energy efficiency R&D budgets in the green transformation process: insight from the top-five countries, Environ. Sci. Pollut. Control Ser. 29 (50) (2022) 76472-76484.
  11. M.K. Guzowska, et al., R&D spending in the energy sector and achieving the goal of climate neutrality, Energies 14 (23) (2021) 7875.
  12. F.S.M. Chachuli, F.M. Idris, The impact of policy toward R&D and innovation on nuclear technology in Malaysia, in: IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2023.
  13. M. Ahmad, et al., Financial risk, renewable energy technology budgets, and environmental sustainability: is going green possible? Front. Environ. Sci. 10 (2022) 909190.
  14. K. Khan, C.W. Su, Does policy uncertainty threaten renewable energy? Evidence from G7 countries, Environ. Sci. Pollut. Control Ser. 29 (23) (2022) 34813-34829. https://doi.org/10.1007/s11356-021-16713-1
  15. Y. Shang, et al., The impact of climate policy uncertainty on renewable and nonrenewable energy demand in the United States, Renew. Energy 197 (2022) 654-667. https://doi.org/10.1016/j.renene.2022.07.159
  16. K. Gavriilidis, Measuring Climate Policy Uncertainty, 2021. Available at: SSRN 3847388.
  17. L. Zhan, et al., Development and outlook of advanced nuclear energy technology, Energy Strategy Rev. 34 (2021) 100630.
  18. IEA, Energy Technology RD&D Budgets: Overviews 2022, International Energy Agency, Paris, 2022.
  19. N.T. Hung, Remittance, renewable energy, and CO2 emissions: a Vietnamese illustration, J. Knowl. Econ. (2023) 1-25.
  20. Z. Xiao, Quantile cointegrating regression, J. Econom. 150 (2) (2009) 248-260. https://doi.org/10.1016/j.jeconom.2008.12.005
  21. P. Saikkonen, Asymptotically efficient estimation of cointegration regressions, Econom. Theor. 7 (1) (1991) 1-21. https://doi.org/10.1017/S0266466600004217
  22. T.S. Adebayo, et al., The asymmetric effects of renewable energy consumption and trade openness on carbon emissions in Sweden: new evidence from quantile-onquantile regression approach, Environ. Sci. Pollut. Control Ser. 29 (2) (2022) 1875-1886. https://doi.org/10.1007/s11356-021-15706-4
  23. W.S. Cleveland, Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc. 74 (368) (1979) 829-836. https://doi.org/10.1080/01621459.1979.10481038
  24. N. Sim, H. Zhou, Oil prices, US stock return, and the dependence between their quantiles, J. Bank. Finance 55 (2015) 1-8. https://doi.org/10.1016/j.jbankfin.2015.01.013