<p>Achieving the Sustainable Development Goals (SDGs) requires accelerated green innovation (GI) to drive green growth. However, the role of green human capital at the executive team level has not been sufficiently emphasized in existing studies. In this paper, we based on the natural language processing (NLP) and manual error correction, the word frequency measurement of feature keywords is carried out, and the system identifies the green background of executives (GBEs). We construct a panel fixed-effects model to test the effect of GBEs on GI. The study samples are derived from all listed companies on the A-share market in China. We find that GBEs can promote GI. Specifically, for every one standard deviation increase in the proportion of GBEs, the number of green patent applications will increase by approximately 3.22%. This impact is believed to be achieved through enterprises’ environmental concern (EC) and reduced green agency costs (GAC), while the uncertainty of climate policies (CPU) amplifies this effect. In addition, we explored the differentiated impact of GBEs under different situational conditions and the characteristics of this innovation incentive effect. The study breaks through the limitations of traditional research on executive characteristics, and provides micro evidence of green human capital to promote GI and accelerate the realization of SDGs.</p>