From Digital Innovation to Climate Action: <scp>FinTech</scp> , <scp>AI</scp> , and Environmental Regulations in Achieving the <scp>SDG</scp> ‐13
Аннотация
ABSTRACT China, being the largest emitting country in the world, prioritizes the achievement of the Sustainable Development Goal 13 (SDG‐13). In this regard, digital innovation technologies such as FinTech (FT) and artificial intelligence (AI) are considered as pivotal tools for enhancing environmental sustainability and low carbon transitions. This study analyzes the effect of FT and AI on SDG‐13 progress in China, which is undergoing rapid digital and financial transformation over the years. Besides analyzing the individual effects, the present study also examines the interaction between FT, AI and environmental regulations (ENVR) to assess their combined role in reducing CO 2 emissions (CEM). Time series data spanning over 1998 to 2023 period is analyzed using Quantile Autoregressive Distributed Lag Model (QARDL) and machine learning based Support Vector Regression (SVR). The estimation of QARDL indicates that none of the selected determinants impact CEM significantly in the short run. In the long run, the findings indicate that AI exerts insignificant but FT exerts significant and negative impact on CE, showing that limited integration and technological immaturity hampered the realization of the AI potential for reducing CEM. However, ENVR are found to increase CEM positively in the long run. Similarly, the moderating role of ENVR in AI and CEM nexus is found to be insignificant in the long run, implying that environmental instruments are not effectively complementing AI innovations to achieve green initiatives. In contrast, ENVR exerts a positive moderating effect on the relationship between FT and CEM, indicating that strong ENVR catalyzes the FT's capability to reduce emissions. Moreover, SVR model exhibits strong predictive accuracy and reports that interaction between ENVR and FT is more influencing feature of the dependent variable than the interaction between ENVR and AI, and AI exhibited the least contribution to CEM. In conclusion, this study highlights the significance of digital innovations and ENVR complementarity in achieving SDG‐13 target in China. On the basis of these findings, several policy insights are provided by the study for integrating digital innovation and regulatory policies to accelerate the pace of achieving SDG‐13 target.