Abstract
Green finance is a key institutional framework supporting China’s newly publicized “Ecological Civilization Construction” initiative, and studies have analyzed the influencing factors of green growth from multiple perspectives; there are few studies that have examined the effectiveness of China’s multidimensional green finance goals. This study analyzes panel data of 30 provinces in China from 2008 to 2020 and uses the Super Slacks-Based Measure (Super-SBM) model to calculate China’s green finance efficiency (GFE) and discusses its dynamic evolution characteristics in spatiotemporal dimensions. The main conclusions are as follows: First, there is a steady upward trend in China’s overall GFE value, despite a low level of GFE in general. Second, there is a curse of “Hu Huanyong line,” with highs in the eastern region and lows in the central and western regions as the overall distribution pattern. Third, GFE has a positive spatial spillover effect, and green finance development in nearby regions is closely related.
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Public data from the Chinese government.
Notes
The six high energy consumption industries include the power and heat production and supply industry, the petroleum processing coking and nuclear fuel processing industry, the chemical raw materials and chemical products manufacturing industry, the non-ferrous metal smelting and rolling processing industry, the ferrous metal smelting and rolling processing industry, and the non-metallic mineral products industry.
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This research was funded by the Natural Science Foundation of Shandong province (CN), grant number ZR2022QG057, and Social Science Application Research Project of Zibo city (CN), grant number 22WL(Y)251.
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Conceptualization, Z. C.; data curation, F. W.; formal analysis, Z. C. and F. W.; funding acquisition, Z. C.; methodology, Z.C.; writing—original draft, Z. C. and F. W.; writing—review and editing, Z. C.
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Cui, Z., Wang, F. The spatiotemporal dynamic and spatial spillover effect of green finance efficiency in China: analysis based on super-SBM model and spatial Durbin model. Environ Sci Pollut Res 30, 67040–67058 (2023). https://doi.org/10.1007/s11356-023-27004-2
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DOI: https://doi.org/10.1007/s11356-023-27004-2