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Assessing the Dual Bonus of Environmental Information Disclosure in China: New Evidence from the Double Machine Learning Model

Author

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  • Xiufeng Zhang
  • Yue Gao
  • Manqian Cao
  • Zhenhua Zhang

Abstract

This research investigates the dual effects of environmental information disclosure (EID) on carbon reduction and quality improvement using panel data from 285 Chinese cities (2003–2018). Utilizing a double machine learning framework, the analysis reveals that EID significantly reduces carbon intensity (CI) while enhancing carbon productivity (CP), demonstrating a dual-effect dividend. These findings are robust across alternative samples, model specifications, and policy interventions. Moreover, EID generates notable spatial spillover effects, with heterogeneous influences on CI and CP across regions. Mechanism analysis suggests that EID promotes green transformation by increasing per capita industrial output and optimizing industrial structure. The magnitude of the dual-effect dividend varies by region, city size, and resource endowment. This research offers policy-relevant insights into sustainable development, contributing a Chinese perspective to global carbon governance and quality growth.

Suggested Citation

  • Xiufeng Zhang & Yue Gao & Manqian Cao & Zhenhua Zhang, 2025. "Assessing the Dual Bonus of Environmental Information Disclosure in China: New Evidence from the Double Machine Learning Model," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 61(13), pages 4231-4246, October.
    • Handle: RePEc:mes:emfitr:v:61:y:2025:i:13:p:4231-4246
    DOI: 10.1080/1540496X.2025.2507927
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