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数字减碳的驱动机制与异质性效应研究OACHSSCD

Research on the Digital Carbon Reduction Driving Mechanisms and Heterogeneous Effects

中文摘要英文摘要

在数字革命与全球气候治理深度融合的背景下,探究数字化转型如何赋能企业实现"数字减碳"具有重大的理论与实践意义.选取2011-2023年中国A股上市公司为样本,考察了数字化转型对企业碳排放绩效的影响.为确保结论的稳健性并有效应对潜在的内生性问题,引入了双重机器学习(DML)等前沿计量方法进行严格检验.研究发现:数字化转型能显著提升企业碳排放绩效.这一促进作用主要通过提升生产效率、促进绿色技术创新和缓解融资约束三条路径实现.进一步分析表明,高质量的碳信息披露能够正向调节并增强数字化的减碳效应.此外,该效应在非国有企业、东部地区、高竞争行业以及高新技术企业中表现得更为显著.该研究不仅系统揭示了"数字减碳"的微观作用机制与边界条件,其采用的双重机器学习方法也为相关领域的因果推断提供了更可靠的分析范式,研究结论为企业制定数字化减碳战略和政府设计精准化政策提供了实证依据.

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.

欧哲琳;毛美婷;王一宁;陈茁;谭雯静

湘南学院 经济与管理学院,湖南 郴州 423000湘南学院 经济与管理学院,湖南 郴州 423000湘南学院 经济与管理学院,湖南 郴州 423000湘南学院 经济与管理学院,湖南 郴州 423000湘南学院 经济与管理学院,湖南 郴州 423000

管理科学

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

digital transformationcarbon emission performancecarbon information disclosuregreen technological innovationDouble Machine Learning

《金融理论与教学》 2026 (2)

76-86,111,12

湖南省自然科学基金资助项目(NO.2025JJ70559)湖南省社会科学成果评审委员会课题(NO.XSP24YBC336).

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