Spatial coupling coordination and interactive response between green finance and green total factor productivity: geographical analysis based on Chinese provinces, 2010-2020.

Based on the panel data of 30 provinces in China from 2010 to 2020, this study uses the coupling coordination degree model (CCDM), spatial autocorrelation analysis, and the panel vector autoregression (PVAR) model to quantitatively examine the dynamic coordination and interactive response of green finance (GF) and green total factor productivity (GTFP). The results show that the coupling coordination degree (CCD) between GF and GTFP during the study period shows an overall increasing trend and significant regional differentiation and remains at the medium level, indicating large room for improvement. The CCDs of provinces fluctuate between the low and high types. The distribution gradually shifts from a "high in the south and low in the north" pattern to a "high in the east and low in the west" configuration. The spatial difference of coupling development continues to expand and presents a significant spatial positive correlation. In addition, GF and GTFP in China exhibit significant self-reinforcing characteristics, but the reinforcing effect gradually decreases. There is only a unidirectional interaction relationship between GF and GTFP, which is GF actively fosters the advancement of GTFP, while the influence of GTFP on GF appears less pronounced. This research provides comprehensive insights into the dynamic interaction between GF and GTFP.

Regional differences, dynamic evolution, and spatial-temporal convergence of green finance development level in China.

Green finance has great potential for supporting environmental improvement, combating climate change, and the economical and efficient use of resources. In this study, based on the panel data of 30 provinces in China from 2010 to 2020, we used the weighted TOPSIS model to measure the green finance development level (GFDL) in China and its three major regions. The Dagum's Gini coefficient, kernel density estimation, Markov chain, and the convergence model are used to analyze the regional differences, dynamic evolution, and spatial-temporal convergence of GFDL in China. The results show that, in general, the GFDL shows an upward trend, but the GFDL in various regions is unbalanced, which is characterized by the spatial distribution of "high in the southeast and low in the northwest" and "high in the coast and low in the inland". The overall difference of GFDL is showing an expanding trend, which is mainly caused by inter-regional difference. The absolute differences of GFDL between the overall country, the eastern region, and the western region are on a widening trend, while that in the central region is on a narrowing trend. In addition, the GFDLs between the overall country, the eastern region, and the western region have no significant σ convergence, while there is an obvious σ convergence trend in the central region. Further, the GFDLs in China and its three major regions have obvious absolute ß convergence trends and conditional ß convergence trends.