Unveiling How the Digital Economy Empowers Green Productivity: Machine Learning and FsQCA Methods

The digital economy plays a pivotal role in advancing green productivity; however, the specific configurations driving this relationship remain underexplored. Employing the TOE theoretical framework alongside k-means clustering and fuzzy-set qualitative comparative analysis (fsQCA), we systematically examine the heterogeneous pathways through which digital economy configurations enhance green productivity in China’s Beijing–Tianjin–Hebei region. The results reveal that (1) green productivity exhibits distinct temporal evolution phases and spatial distribution patterns; (2) five characteristic digital economy city clusters emerge from the clustering analysis; (3) improvements in green productivity require specific synergistic combinations of technological, organizational, and environmental factors; and (4) antecedent conditions demonstrate complex substitution patterns across different development stages. These findings offer a configurational perspective on how digital economy architectures differentially influence regional green productivity development.