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 sigma convergence, while there is an obvious sigma convergence trend in the central region. Further, the GFDLs in China and its three major regions have obvious absolute beta convergence trends and conditional beta convergence trends.