This paper, based on provincial panel data and enterprise microscopic dataset from 2011 to 2019, establishes a bi-directional fixed effect model to study the impacts of digital finance on enterprise’s dual innovation and its mechanism from commercial environment. Regression coefficients of digital finance on enterprise’s radical innovation and incremental innovation are positive; the radical innovation can rise by 58.83% and incremental innovation by 49.92% upon digital finance’s rising by 1%, suggesting that digital finance largely boost enterprise’s innovation through coverage and depth, stepping out of “low locked” situation for a higher innovation. Regression coefficients of digital finance on enterprise’s dual innovation in central and western regions are 2.607 7 and 1.237 8 respectively, higher than 0.391 7 in the eastern region, suggesting digital finance exerts a stronger marginal effect on enterprise’s dual innovation in the central and western regions where financial resources are insufficient due to geographic resources occurrence variance, compared with the developed eastern region. On mechanism test, regression coefficients of digital finance to radical innovation and commercial environment are 0.588 3 and 0.429 9, outstanding above 1%, marking their positive relation. Impacting coefficient of commercial environment on radical innovation is 1.032, and mediating effect of commercial environment reaches 74.19%. On incremental innovation, direct effect is opposite to indirect effect, and commercial environment plays a covering effect on incremental innovation. As digital finance gains more supports, improving commercial environment will make enterprises put more resources in radical innovation, thus less in incremental innovation. This paper puts forward suggestions on continuously optimizing digital finance policies, establishing regional digital finance coordination and financial servicing platform to provide enterprises’ dual innovation with external security and to stimulate their inner drive.

This paper explores the coupling coordination between technical innovation and water resources environment in Zhejiang province, and forecasts its tendency so as to provide references for Zhejiang's green development, water resources environmental protection and high-quality development based on Zhejiang's 11 cities' technical innovation and water resources data from 2010 to 2019. A comprehensive evaluation index system is established with its indexes' weights determined via entropy, AHP and minimum relative information entropy, which is used to estimate the comprehensive evaluation values of technical innovation system and water resources environment system. Coupling coordination model is used to calculate the coupling coordination of the two systems. GM(1, 1) model is employed to forecast the coordination tendency in the next six years. The development levels of Zhejiang's technical innovation system and water resources system in 2019 are 0.398 1 and 0.458 7 with an annual increasing rate 14.62% and 3.94%. Technical innovation has a faster developing rate. The two systems vary with regions. The coupling coordination shows an increasing trend with Zhejiang's overall coupling coordination rising from 0.411 3 to 0.636 8, near disordered from 2010 to 2013, fairly ordered from 2014 to 2017, preliminarily ordered from 2018 to 2019, forecasted to be well ordered in next six years with a lagging annual increasing rate from 4.98% to 4.58%. The coupling coordination shows geographically a pattern of north-high-south-low, east-high-west-low, with forecasted no outstanding changes in next six years.From 2010 to 2019, Zhejiang pays much attention to technical innovation, optimizes water resources environment, and promotes a collaborative development between technical innovation and water resources environment, but spatial differences exist. This paper presents suggestions on boosting technical innovation, improving water resources environment, preventing a lagging coupling coordination, and reducing regional variance so as to reach a better coordinated development between technical innovation and water resources environment.

Green growth of industrial enterprises is a key element in sustainable development, which requires industrial enterprises to increase innovation efficiency and to control energy-saving-emission-reduction. This paper, putting three industrial wastes into the evaluation of technical innovation efficiency, uses SBM model to estimate their green technical innovation efficiency of industrial enterprises in China's 30 provinces during 2009 to 2015, and employs Durbin model to analyze their spatial effects of some variables on green technical innovation efficiency. The outcome shows a rising trend of green technical innovation efficiency, generally in a low level, varying among eastern, central, western and northeastern. In the whole China, this efficiency exhibits an apparent spatial diffusion with good achievement in regional technical exchanges. A direct spatial effect on this efficiency comes from governmental supports on scientific technology, technical conversion level, infrastructures and openness, and an indirect spatial effect comes from human capital level.