The Spatiotemporal Evolution Characteristics and Improvement Paths of China’s Green Finance Level——Empirical Study on Panel Data Based on Dynamic QCA and NCA Methods

Green finance (GF) is the core driving force in solving environmental problems. Under the digital background, how to improve the GF with the help of digital technology is a topic worthy of further study. Based on data from 29 provinces from 2014 to 2021, this study uses the entropy weight method to estimate the level of GF in China,and uses Dagum Gini coefficient and Kernel density estimation method to explore the spatio-temporal evolution characteristics of green finance level. Finally, based on the theory of the digital innovation ecosystem (DIE), we use NCA and dynamic QCA methods to explore the configuration effects of various elements within the DIE over time. The results show that the overall level of GF in China has a steady upward trend, achieving nearly double growth, yet there are significant regional differences, and the level of GF in Northeast China fluctuates unsteadily; From the perspective of regional differences, the level of GF in different regions of China is quite different, among which the western region has the largest regional difference, and super-variable density is the main source of regional differences. From the perspective of dynamic evolution, the overall level of GF in China is on the rise, among which, there is a “catch-up effect” among provinces. A single factor does not constitute the necessary conditions to improve the level of GF, and then through linkage matching, three promotion paths are obtained, and further divided into two models- environmental support model and multi-agent comprehensive development model. Deepening the rational understanding of the complex interaction of multiple factors behind the improvement of the level of GF has important implications for sustainable economic development (SED).