The Association Between Data Factor Utilization and Direct Combustion Carbon Intensity: Evidence from China’s Energy-Intensive Industries

Energy-intensive industries account for the largest share of China’s industrial carbon emissions and are the hardest to decarbonize, because production is locked into established technologies and organizational routines. Data resources are usually measured by how often firms mention digital terms in annual reports, which reveals little about what firms actually deploy. This study examines the firm-level association between data factor utilization, measured from expenditure on big-data software and hardware and from patented digital technology, and direct combustion carbon intensity, reconstructed from disclosed physical fuel quantities, in a panel of Chinese A-share listed firms in six energy-intensive industries from 2010 to 2022. A one-standard-deviation increase in data factor utilization is associated with a 0.41 percent lower direct combustion carbon intensity, and the estimate survives nine robustness checks. A two-stage channel analysis links data factor utilization to incremental innovation, radical innovation, and the realized deployment of big-data applications, and each of these to lower intensity; the evidence is strongest for realized deployment. Board network centrality weakens the association. Because the emission measure excludes purchased electricity and the energy requirements of data infrastructure, the estimates show where data investment coincides with lower on-site combustion, not whether it achieves net mitigation.