Yu Wenmei, Xia Lina, Cao Qiang
School of Finance, Anhui University of Finance and Economics, Bengbu, 233030, China.
Sci Rep. 2024 Oct 9;14(1):23609. doi: 10.1038/s41598-024-75753-y.
As the world's largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China's carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.
作为全球最大的能源消费国和碳排放国，碳减排任务迫在眉睫。为早日实现双碳目标，有必要研究影响中国碳排放的关键因素及其非线性关系。本文利用中国254个城市的面板数据，比较了六种机器学习算法与传统计量经济模型对中国2011年至2020年碳排放的预测性能。具体而言，基于极端随机树模型，分析了国内经济、外部经济和政策不确定性因素的相对重要性，以及这些因素与碳排放之间的非参数关系。结果表明，在国内经济因素中，能源消费（ENC）仍然是碳排放增加的根本原因，尽管政府干预（GOV）和数字金融（DIG）可以显著降低碳排放。其次，在外部经济和政策不确定性因素中，外国直接投资（FDI）和经济政策不确定性（EPU）是影响碳排放的重要因素，局部依存度图（PDP）证实了污染避难所假说，也揭示了EPU在减少碳排放中的作用。还分析了不同城市规模下影响碳排放因素的异质性，发现ENC是不同规模城市的共同驱动因素，但存在一些差异。最后，我们提出了适当的政策建议，以帮助中国迅速走上绿色可持续发展道路。
