阐述核能在中国循环经济中的作用：一种机器学习方法
Articulating the role of nuclear energy in the circular economy of China: A machine learning approach
AI总结：
本文使用DYNARDL方法分析1990-2017年中国数据，发现核能在长短期内均对循环经济产生负面影响，为相关政策策略提供了计量经济学证据。
摘要：
由于核能具备提供稳定低碳能源的能力，它越来越被认为是循环经济框架的关键组成部分。减少对化石燃料的依赖促进了可持续实践，并与资源效率、减少污染和最小化废物等循环经济目标相一致。现有文献主要集中在核能对脱碳的贡献上，而核能在促进循环经济方面的潜力在很大程度上被忽视了。鉴于这一背景，本文探讨了核能对循环经济的影响，从而提供了强有力的计量经济学证据。该研究使用先进的计量经济学工具动态自回归分布滞后（DYNARDL）方法进行实证估计，以获得长期和短期估计值。基于1990年至2017年中国样本得出的回归估计支持了核能在长期和短期内均对循环经济产生负面影响的假设。高级计量经济学检验证实了模型的稳定性、同方差性以及无序列相关，确保了我们研究结果的可靠性。该研究强调了政策策略的重要性，包括扩大核能采用、推进环境技术以及通过整合综合数据集和方法有效利用核能；随着中国迈向更绿色和更可持续的未来，本文为可扩展和公平的解决方案提供了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
