金融理论与教学2026，Vol.44Issue(2)：76-86,111,12.
数字减碳的驱动机制与异质性效应研究
Research on the Digital Carbon Reduction Driving Mechanisms and Heterogeneous Effects
摘要
Abstract
Against the backdrop of the deep integration of the digital revolution and global climate governance,investigating how digital transformation empowers firms to achieve digital carbon reduction holds great theoretical and practical significance.Using A-share listed companies in China from 2011 to 2023 as samples,this study examines the impact of digital transformation on corporate carbon emission performance.To ensure the robustness of our findings and effectively address potential endogeneity issues,cutting-edge econometric methods are employed,including Double Machine Learning(DML),etc.for rigorous testing.The study finds that digital transformation significantly enhances corporate carbon emission performance.This promotional effect is primarily realized through three channels:improving production efficiency,promoting green technology innovation,and alleviating financing constraints.Further analysis reveals that high-quality carbon information disclosure positively moderates and strengthens the effect of digital carbon reduction.Moreover,this effect is more pronounced in non-state-owned enterprises(non-SOEs),firms in the eastern region,firms in highly competitive industries,and high-tech firms.This research not only systematically uncovers the micro-level mechanisms and boundary conditions of digital carbon reduction but also provides a more reliable analytical paradigm for causal inference in related fields through its use of the DML method.The findings offer empirical evidence for firms in formulating digital carbon reduction strategies and for governments in designing targeted policies.关键词
数字化转型/碳排放绩效/碳信息披露/绿色技术创新/双重机器学习Key words
digital transformation/carbon emission performance/carbon information disclosure/green technological innovation/Double Machine Learning分类
管理科学引用本文复制引用
欧哲琳,毛美婷,王一宁,陈茁,谭雯静..数字减碳的驱动机制与异质性效应研究[J].金融理论与教学,2026,44(2):76-86,111,12.基金项目
湖南省自然科学基金资助项目(NO.2025JJ70559) （NO.2025JJ70559）
湖南省社会科学成果评审委员会课题(NO.XSP24YBC336). （NO.XSP24YBC336）
