| Title |
Can fintech pave the way for a transition towards low-carbon economy? Examination based on machine learning algorithm |
| ID_Doc |
32121 |
| Authors |
Yang, SQ; Fan, SS; Shahbaz, M |
| Title |
Can fintech pave the way for a transition towards low-carbon economy? Examination based on machine learning algorithm |
| Year |
2024 |
| Published |
|
| DOI |
10.1007/s11356-024-32588-4 |
| Abstract |
Realizing the coordination between the economic and environmental systems through a green growth model is an important goal for China to enter the high-quality development stage. Meanwhile, financial technology (fintech) is rapidly developing in China. To explore the relationship between the two, this research uses panel data from 276 cities in China from 2011 to 2022 and empirically tests through constructing econometric models and machine learning algorithms. The empirical result shows that fintech has an impact on green growth. Specifically, there is a U-shaped relationship between fintech and green growth, meaning that before a certain stage, fintech may have a certain inhibitory effect on green growth. After fintech exceeds a certain development level, it will promote the improvement of green growth. Further mediation tests show that innovation plays a mediating role in the impact of fintech on green growth. Additionally, this research also conducts consistency tests based on different criteria including the location, scale, and financial development level of cities. Based on the research findings, policy suggestions are proposed in this paper to promote the development of fintech and stimulate the growth of the green economy. Overall, our research sheds more light on the fintech-green growth linkage and provides new insights into comprehending the role of fintech in advancing towards a low-carbon economy. |
| Author Keywords |
Fintech; Green economic growth; Innovation; China; Machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001173086700005 |
| WoS Category |
Environmental Sciences |
| Research Area |
Environmental Sciences & Ecology |
| PDF |
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