How Ai Reduces Pollutant Emissions: The Dual Mechanisms of Productivity Enhancement and Financing Constraint Alleviation

Abstract

Artificial intelligence (AI) technology plays a pivotal role in driving the green transformation of enterprises and achieving high-quality development. Drawing on data from China's A-share listed companies, this study constructs firm-level AI technology indicators through semantic extraction from annual reports using the GLM-4 large language model to systematically analyze the impact of AI on corporate pollution behaviors and its underlying mechanisms. The research reveals that AI technology significantly reduces the intensity of corporate pollutant emissions, a conclusion that remains robust across various tests. Mechanistically, AI enhances firms' total factor productivity (TFP) and alleviates financing constraints, thereby enabling the achievement of emission reduction goals. Heterogeneity analysis suggests that the emission-reduction effects of AI are more pronounced in non-state-owned enterprises, non-heavy-polluting industries, and sectors with lower competitive intensity. The findings of this study offer valuable insights for leveraging AI technology to empower corporate environmental governance and green transformation, while also providing critical guidance for policymakers to craft differentiated strategies tailored to the characteristics of various enterprises and industries.

Keywords: Artificial Intelligence (AI)Pollution EmissionTotal Factor Productivity (TFP)Financing Constraints