The outgoing audit of natural resources assets and green governance: New evidence from causal inference based on double machine learning

Xinyu Zhang, Yixuan Liu, Jiawei Zhu, Yifei Teng, Zedong Chen, Jiansheng You and Xiaohui Wang

Economic Analysis and Policy, 2025, vol. 87, issue C, 1113-1129

Abstract: Within China's extensive development of a modern socialist state and the quest for high-quality advancement, the effectiveness of corporate green governance is significantly impacted by the outgoing audit of natural resources asset (OANRA), a critical institutional innovation in the establishment of ecological civilization. Using a double machine learning (DML) approach, this study examines how the system improves corporate green governance performance (GGP) through greater government engagement, decreased environmental agency costs, and improved corporate social responsibility according to the data from A-share listed businesses in Shanghai and Shenzhen from 2011 to 2020. The results indicate OARNA significantly promotes the corporate GGP. And this effect is more pronounced in firms which have green investors, classified in the manufacturing industry and located in highly marketed regions. The promoting effect occurs as OANRA augmenting government intervention, decreasing corporate green agency costs, and improving corporate social responsibility compliance. Our findings highlight that top executives' use of the natural resource asset audit system greatly promotes high-quality development and corporate green performance, offering both theoretical and practical support for pertinent policy design in developing countries like China.

Keywords: Outgoing audit of natural resources asset; Corporate green governance; Double machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecanpo:v:87:y:2025:i:c:p:1113-1129

DOI: 10.1016/j.eap.2025.06.024

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