Articulating the role of nuclear energy in the circular economy of China: A machine learning approach

Nuclear energy is increasingly recognized as a critical component of circular economy frameworks due to its capacity to provide a stable, low-carbon energy source. Reducing dependency on fossil fuels promotes sustainable practices and aligns with circular economy goals such as resource efficiency, pollution reduction, and waste minimization. The existing literature has primarily focused on the contribution of nuclear energy to decarbonization, whereas the potential of nuclear energy in facilitating a circular economy has been largely neglected. In light of this context, this paper explores the impact of nuclear energy on the circular economy, thereby offering strong econometric evidence. The study used the advanced econometric tool Dynamic Auto-Regressive Distributive Lag (DYNARDL) method for empirical estimation to obtain long- and short-run estimates. The regression estimates, derived from a sample of China spanning 1990 to 2017, support the hypothesis that nuclear energy negatively impacts the circular economy in both the long- and short-run. Advanced econometric tests confirm the stability of the models, homoscedasticity, and the absence of serial correlation, ensuring the reliability of our findings. The study emphasizes the importance of policy strategies, including expanding nuclear energy adoption, advancing environmental technologies, and the effective use of nuclear energy by integrating comprehensive datasets and methodologies; this paper provides a foundation for scalable and equitable solutions as China moves toward a greener and more sustainable future.