*4.2. Multiple Regression Analysis*

4.2.1. The Effect of Executive's Environmental Protection Background on Green Innovation

Table 4 reports the impact of whether and how corporates on green innovation hire many executives with environmental protection backgrounds. Among them, Columns (1)–(2) show the effect of whether or not a firm employs an executive with environmental protection background on green innovation, and Columns (3)–(4) show the impact of the number of executives with environmental protection background hired by a corporate on green innovation. From the regression results in Column (2), the regression coefficient between whether or not to hire executives with environmental protection backgrounds (hbbjdum) and green innovation is significantly positive, indicating that hiring executives with environmental protection background promotes green innovation. The results in Column (4) show that the regression coefficient between the number of executives with environmental protection backgrounds (lnhbbj) hired by corporates and green innovation is significantly positive at the 5% level, indicating that the more executives with environmental protection backgrounds hired by corporates, the more they can promote green innovation. Therefore, Hypothesis 1 of this study is supported.

**Table 4.** The regression results of environmental protection background on green innovation.



**Table 4.** *Cont.*

Notes: \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

As pointed out by the upper echelons theory, executives' pre-career experience continuously internalizes the mindset and behavior of executives in their later work, which in turn affects the behavioral decisions and even the strategic layout of the market. Therefore, executives with environmental protection backgrounds are more likely to integrate their environmental experience into corporate strategic decisions and pay more attention to green sustainability performance. Further, to control the fixed effect of the industry is to control the factors that are relatively constant relative to the industry. For example, there are unique differences in different industries that do not change with time, and the food industry is an industry that is less affected by the economic cycle, but the steel industry is cyclical. By controlling the fixed effect of the industry, this study can control the differences between industries, and help to estimate the regression results more reasonably [62].
