A study on the impact of data assets on carbon emission intensity of energy enterprises – evidence from China

Purpose The purpose of this study is to explore how energy enterprises can leverage their data assets to reduce carbon emission intensity. Against the backdrop of China’s “Dual-Carbon” goals, data assets have become a critical resource for the energy industry’s low-carbon transition. Design/methodology/approach Using machine learning techniques (e.g. Python), this study collects and analyzes data on data asset usage from 299 energy companies listed on China’s A-share market between 2013 and 2021. By integrating financial data from annual reports, the authors empirically examine the relationship between data assets and carbon emission intensity, along with the underlying mechanisms. Findings The results reveal that (1) data assets of energy enterprises significantly reduce carbon emission intensity, as validated by robustness tests including Heckman two-stage model, propensity score matching and placebo test; (2) mechanism analysis shows data assets reduce carbon intensity by boosting R&D investment, total factor productivity and easing financing constraints; and (3) heterogeneity analysis finds the inhibitory effect is more pronounced in non-state-owned, traditional energy and high-tech enterprises. Originality/value The findings of this study provide important insights into energy enterprises’ use of data assets to reduce carbon emissions intensity.