Artificial Intelligence Investment and Enterprise Green Innovation Efficiency

Abstract

Artificial intelligence investment lies at the core of enterprise digital transformation, and its influencing mechanism on green innovation efficiency urgently needs to be explored. Taking China's A-share listed companies from 2007 to 2023 as samples, this paper measures the development level of enterprise artificial intelligence from two dimensions: disclosure frequency of artificial intelligence-related words and investment level, and uses the random-effects panel Tobit model to examine its impact on green R&D efficiency and green achievement transformation efficiency. The findings are as follows: First, each logarithmic unit increase in the frequency of artificial intelligence words raises green innovation efficiency by approximately 0.042 units (about 8%), and a one-standard-deviation increase in investment level improves green innovation efficiency by about 4%, both of which are significant at the 1% level. Second, the conclusions still hold after robustness tests using the high-dimensional fixed-effects model and the DID policy shock of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones, and pass the parallel trend test. Third, the asset-liability ratio significantly positively moderates this effect, highlighting the key role of financing capacity. Fourth, the promotion effect is more significant in state-owned enterprises, large-scale enterprises and manufacturing enterprises. This paper provides empirical evidence for optimizing enterprise artificial intelligence investment strategies and promoting green transformation.