Abstract

Global climate change governance necessitates financial empowerment to address this worldwide challenge. This study integrates critical components such as sustainable green innovation in small and medium-sized enterprises(SMEs), industrial agglomeration, and regional innovation into the analytical framework of digital inclusive finance(DIF) and regional carbon emissions. Using China as a case study, we analyze data from 30 provinces (excluding Tibet, Hong Kong, Macau, and Taiwan) between 2013 and 2021, as well as data from 235 prefecture-level cities and microdata from companies listed on the New Third Board. Conducted through the dynamic panel model of the Generalized Method of Moments (GMM), empirical tests reveal that DIF acts as a mitigating factor in regional carbon emissions, with sustained green innovation of SMEs playing a crucial intermediary role. However, enterprises lean towards strategically viable green innovation, which has lower thresholds and higher practicality compared to substantive green innovation. Additionally, our findings show that industrial agglomeration and regional innovation further enhance the suppressive impact of DIF. Heterogeneity tests indicate that deeper and more digitized digital inclusive finance yields superior results in reducing regional carbon emissions, while non-resource-based cities outperform others in terms of carbon emissions. Based on these conclusions, we propose the following policy recommendations: governments should actively promote DIF and encourage green innovation in SMEs to curtail carbon emissions. Further efforts should be made to foster industrial agglomeration and regional innovation, reinforcing their impact on carbon emissions reduction. Raising the bar for green innovation and guiding enterprises towards substantive green practices is crucial, while tailored support should be provided to non-resource-based cities. This study offers valuable insights for policy formulation and green innovation implementation by governments and enterprises alike.

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