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Data asset information disclosure divergence and green innovation: causal evidence from double-debiased machine learning

Author

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  • Shi, Huiyi
  • Xia, Yufei
  • Li, Yawen
  • Hua, Yifan
  • Fu, Yating

Abstract

We examine the impact of the divergence between disclosed and actual data assets on firms' green innovation. Based on a Chinese listed companies dataset from 2010 to 2023, we uncover that data asset information disclosure divergence suppresses green innovation, and this conclusion remains robust across several rigorous robustness checks. Mechanism analysis shows that the divergence impedes green innovation by increasing debt financing costs, intensifying media scrutiny, and reducing the institutional investor’s stability. Moreover, accounting conservatism amplifies the suppressive effect of divergence on green innovation. Heterogeneity analysis reveals that the negative effect is more pronounced among state-owned, mainboard-listed, and high-pollution enterprises.

Suggested Citation

  • Shi, Huiyi & Xia, Yufei & Li, Yawen & Hua, Yifan & Fu, Yating, 2025. "Data asset information disclosure divergence and green innovation: causal evidence from double-debiased machine learning," Finance Research Letters, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:finlet:v:84:y:2025:i:c:s1544612325010153
    DOI: 10.1016/j.frl.2025.107757
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    References listed on IDEAS

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