In the context of global climate change threatening human survival, and in a post-pandemic era that advocates for a global green and low-carbon economic recovery, conducting an in-depth analysis to assess whether green finance can effectively support low-carbon economic development from a dynamic perspective is crucial. Unlike existing research, which focuses solely on the average effects of green credit (GC) on carbon productivity (CP), we introduce a non-parametric panel data model to investigate GC's impact on CP across 30 provinces in China from 2003 to 2021, verifying a significant time-varying effect. Specifically, during the first phase (2003-2008), GC negatively impacted CP. In the second phase (2009-2014), this negative influence gradually diminished and transformed into a positive effect. In the third phase (2015-2021), GC continued to positively influence CP, although this effect became insignificant during the pandemic. Further subgroup analysis reveals that in the regions with low environmental regulations, GC did not significantly boost CP throughout the sample period. In contrast, in the regions with high environmental regulations, GC's positive effect persisted in the mid to late stages of the sample period. Additionally, compared to the regions with low levels of marketization, the impact of GC on CP was more pronounced in highly marketized regions. This indicates that the promoting effect of GC on CP depends on strong support from environmental regulations and well-functioning market mechanisms. By adopting a non-parametric approach, this study reveals variations in the impact of GC on CP across different stages and under the influence of the pandemic shock, offering new insights into the relationship between GC and China's CP.
Keywords: COVID-19; Carbon productivity; Green credit; Non-parametric analysis; Time-varying effect.
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