This study the theoretical hypotheses by building a relatively exogenous data factor flow indicator and a corporate pollution emission intensity indicator system using China's data flow restriction policy and corporate pollution emission data. Data factor is emerging as an essential new factor of production in today's digital economy, and data cannot function valuablely without an efficient flow. This study constructs a theoretical model of the influence of data factor flow on the pollution emissions of downstream manufacturing enterprises based on the vertical correlation of inputs and outputs. Additionally, we employ additional contaminants as a robustness test and sulfur dioxide, a common representation of air pollution, as the baseline. The empirical analysis's findings indicate that: (1) Data factor flow significantly reduces pollution in manufacturing enterprises. (2) The main way that data factor flow helps organizations minimize pollution is by increasing their productivity and ability to innovate in technology. (3) Domestic data flow is more important than cross-border data flow in helping industrial companies reduce pollution. (4) In general, foreign-owned enterprises have a greater need for data factor flow to reduce pollution than do state-owned and private businesses.