| Based on the provincial panel data of 2011-2020 in China,an EBM-ML index model including the undesired output is constructed,and suitable input-output indicators are selected to measure industrial green total factor productivity in each province and city in China.Using Thiel’s index and kernel density estimation methods,we explore and analyze the regional differences and dynamic evolutionary characteristics of the distribution of industrial green total factor productivity across the country and the three regions of east,central and west.The two-way fixed effects model is used to estimate the impact of digital finance on industrial green total factor productivity,and the heterogeneity is analyzed in terms of structural,regional,and stage effects.Under the environmental regulation constraint,the threshold effect model is used to further analyze the nonlinear impact of digital finance on industrial green total factor productivity.The intermediary effect model is constructed to test the conduction mechanism of the impact of digital finance on industrial green total factor productivity.The study finds that,first,China’s industrial green total factor productivity has been increasing year by year,mainly driven by technological progress,with the fastest growth rate in the west,followed by the east,and negative growth in the central.The"single-drive" growth pattern of technological progress in the east and west,and the"double-drive" growth pattern in the central region.Second,the overall differences in China’s industrial green total factor productivity are increasing,with larger withinregion differences being the main source of overall differences.The nuclear density graphs of the whole country and the central and western regions show the characteristics of "rightward curve shift,peak decline,width widening,right trailing,extension broadening,non-polarization-multi-polarization",while the width of the main peak of the eastern region remains unchanged.Third,digital finance can significantly increase industrial green total factor productivity.The conclusion still holds after endogeneity treatment,replacement of dependent variable,deletion of four municipalities,and variable tail reduction processing.Under the constraints of environmental regulation,the impact of digital finance on industrial green total factor productivity presents a nonlinear characteristic of first increasing and then decreasing.Fourth,there are structural,regional and stage effects on the impact of digital finance on industrial green total factor productivity.Both the coverage breadth and usage depth of digital finance have significant promotion effects,while the digitization degree significantly inhibits.Digital finance has a significant promoting effect in the eastern and western regions,while the central region shows an inhibiting effect.Compared to 2011-2013,the impact of digital finance increases significantly in 2014-2020 after Yu Ebao’s appearance.Fifth,digital finance promotes industrial green total factor productivity improvement by promoting technological innovation,boosting industrial structure upgrading and stimulating entrepreneurial dynamism,with its intermediary effects accounting for 21.90%,11.80%and 19.85%,respectively. |
