Artificial Intelligence Exposure and the Composition of Corporate Green-Skill Recruitment: Evidence from Chinese Listed Firms

Vollständiger Abstract

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Artificial intelligence (AI) is reorganising corporate work, but whether green skills retain their relative weight during intelligent upgrading remains unclear. This study examines how disclosure-based AI technology exposure is associated with the share of green-skill positions in corporate recruitment. It combines annual-report text, online job postings, and financial and governance data for 31,303 firm-year observations of Chinese A-share listed firms from 2016 to 2024. Panel regressions with industry and year fixed effects show that greater AI exposure is associated with a significantly lower green-skill recruitment share. The result remains stable when the explanatory and dependent variables are remeasured, the 2020 observations are excluded, governance controls are added, a fractional response model is used, and firm years are weighted by recruitment volume. The negative association is stronger under higher media attention, greater industry concentration, stronger tax incentives, and more government subsidies. It is also evident across environmental-governance, carbon-management, and new-energy positions. Further analysis shows that the absolute number of green-skill postings rises while total recruitment expands faster, so the lower share represents relative recruitment reallocation rather than a demonstrated contraction in green hiring. Green-skill recruitment is positively associated with green innovation output. By distinguishing AI exposure from verified adoption and recruitment composition from employment levels, the study identifies green human capital as a link between digital transformation and sustainability and provides evidence for coordinated technology, training, and workforce policies.