A Study on the Effects of Digital Finance on Green Low-Carbon Circular Development Based on Machine Learning Models

Xuewei Zhang, Xiaoqing Ai (), Xiaoxiang Wang (), Gang Zong and Jinghao Zhang
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Xuewei Zhang: School of Economics and Management, Inner Mongolia University, Hohhot 010021, China
Xiaoqing Ai: School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Xiaoxiang Wang: School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Gang Zong: School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Jinghao Zhang: School of Economics and Management, Inner Mongolia University, Hohhot 010021, China

Mathematics, 2023, vol. 11, issue 18, 1-16

Abstract: With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are infiltrating the field of finance. Digital finance (DF) is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally-friendly enterprises, thereby promoting green low-carbon circular development (GLCD). However, few studies have explored the coupling mechanism between DF and GLCD. To fill this gap, this paper explores the effect of DF on GLCD, and established a mediating effect model to investigate the mechanism of DF in promoting GLCD. Additionally, this paper established a random forest model and a CatBoost model based on machine learning to examine the relative importance of DF and the factors affecting GLCD. The results show that DF has significant positive effects on GLCD, and technological innovation plays a key role in the effect of DF on GLCD; meanwhile, the effect of DF on GLCD shows nonlinear features with an increasing “marginal effect”; moreover, both DF and conventional factors have significant impacts on GLCD. Our study highlights the effect of DF on GLCD and underscores the importance of developing policies for DF and GLCD. This study provides an empirical basis and path reference for DF to achieve “carbon peak, carbon neutralization” in China.

Keywords: DF; GLCD; machine learning; double-carbon target (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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