<p>While extensive literature examines either the employment impacts of artificial intelligence (AI) or environmental policies separately, this paper focuses on the relationship between AI-driven environmental governance and labor markets. Using China’s phased implementation of air quality monitoring networks as a quasi-natural experiment, we employ panel data from Chinese industrial firms and the difference-in-differences (DID) method. The baseline regression result demonstrates that such monitoring significantly cuts industrial enterprise employment, a finding that survives robustness tests. Mechanism analysis reveals that such monitoring intensifies environmental governance pressure and raises industrial costs. This leads to a negative scale effects that outweigh the positive innovation-compensation effects, ultimately shrinking employment. Heterogeneity analysis examines labor reallocation effects by analyzing employment changes across regions, industries, and enterprises. It finds that the smart ecological environment monitoring network reduces labor distribution in eastern pilot cities, first-batch pilot cities, and regions with strict environmental regulations. In terms of industry, polluting industries experience employment declines, with labor shifting to other sectors. At the enterprise level, non-state-owned and smaller firms see labor outflows. These findings suggest that government should use policy and fiscal tools to encourage polluting firms and medium-sized enterprises (MSEs) to invest in green and intelligent fields, accelerating industrial upgrading.</p>