Data has become an increasingly important production factor in the green transition, yet its independent contribution to green total factor productivity (GTFP) remains insufficiently understood. Using panel data for 30 Chinese provinces from 2011 to 2023, this study constructs a composite index of data factors and measures GTFP using the SBM-GML approach. A two-way fixed effects framework is combined with mediation, threshold, and spatial econometric models to examine the effect of data factors on green productivity. Four main conclusions are obtained: (1) Data factors significantly promote GTFP. Economically, a one-standard-deviation increase in the index of data factors raises GTFP by 0.311, equivalent to 25.3% of the sample mean of GTFP. This result remains robust after a series of robustness and endogeneity tests, with the instrumental-variable estimate reaching 6.203. (2) Industrial structure upgrading, green technological innovation, and environmental regulation are important transmission channels. (3) The effect is nonlinear, with estimated thresholds of 0.238 and 0.239, and the promoting effect is strongest in the middle regime. (4) Data factors also generate positive spatial spillovers, part of which are transmitted through financial agglomeration. These findings underscore the important role of data factors development in advancing energy-efficient green transformation.