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AI adoption narrows the gap between sustainability talk and practice among Chinese listed firms: firms that embrace AI show materially lower ESG decoupling because AI reduces ambiguity, boosts scrutiny, and raises disclosure quality; the governance gains concentrate in R&D‑savvy, reputable, and well‑governed firms.

Corporate AI Adoption and ESG Decoupling Under China’s Dual-Carbon Policy: A Fraud Triangle Analysis
Jincun Fu, Wenqian Gao, Beibei Zhang, Jiayao Ye · July 28, 2026 · Sustainability
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Among Chinese A‑share firms (2009–2024), AI adoption is associated with substantially lower ESG decoupling, operating through reduced environmental uncertainty, increased external scrutiny, and improved disclosure quality, with larger effects in R&D‑experienced, reputable, and well‑governed firms in competitive industries.

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The credibility of corporate environmental claims is fundamental to achieving China’s dual-carbon goals, which aim to peak emissions by 2030 and achieve neutrality by 2060, yet widespread ESG decoupling, a phenomenon where disclosed ESG performance deviates from actual actions, directly undermines policy effectiveness. Guided by the Fraud Triangle Theory, this paper examines whether and how AI adoption curbs decoupling practices. Using data from Chinese A-share listed firms from 2009 to 2024 and mediation analysis, we find that AI adoption significantly reduces ESG decoupling. Specifically, we identify three distinct mediating pathways through which AI exerts its inhibitory effect: it reduces environmental uncertainty and ambiguity, thereby compressing opportunities for managers to engage in decoupling; it heightens media scrutiny and analyst attention, increasing deterrence pressure on firms; and it improves disclosure quality and transparency, undermining the rationalization of decoupling. Heterogeneity analyses reveal stronger effects in firms with R&D-experienced executives, higher reputation, and stronger governance, as well as in highly competitive industries, suggesting that complementary capabilities amplify AI’s governance. These findings provide novel evidence on how AI governs environmental information fraud and offer policy implications for using digital technologies to enhance ESG authenticity.

Summary

Main Finding

AI adoption significantly reduces ESG decoupling among Chinese A‑share listed firms (2009–2024). Guided by Fraud Triangle Theory, the paper shows AI constrains managerial fraud incentives and opportunities through three mediating channels — reducing environmental uncertainty, increasing external scrutiny, and improving disclosure quality — with stronger effects where firm capabilities and competitive pressures are higher.

Key Points

  • Theoretical framing: Fraud Triangle Theory — fraud (here, ESG decoupling) arises from pressure/incentive, opportunity, and rationalization; AI can act on all three.
  • Core result: Firms that adopt AI exhibit materially lower levels of ESG decoupling.
  • Three mediating pathways identified:
  • Reduced environmental uncertainty/ambiguity → fewer opportunities for managers to misstate ESG performance.
  • Increased media scrutiny and analyst attention → greater deterrence and monitoring pressure.
  • Improved disclosure quality and transparency → weakened managerial rationalizations for decoupling.
  • Heterogeneity: AI’s governance effect is stronger in firms with:
    • Executives experienced in R&D,
    • Higher corporate reputation,
    • Stronger internal governance,
    • Firms operating in highly competitive industries. These complementarities imply AI is most effective when combined with organizational capabilities and external pressures.

Data & Methods

  • Sample: Chinese A‑share listed firms, 2009–2024.
  • Empirical approach: Mediation analysis to decompose the total effect of AI adoption on ESG decoupling into the three mediating channels noted above.
  • Identification strategy (high level): Exploits variation in firms’ AI adoption to estimate causal/associational impacts on measurable decoupling outcomes; conducts heterogeneity tests across firm- and industry-level characteristics.
  • Theoretical anchor: Fraud Triangle Theory used to motivate mediators and interpret mechanisms.

Implications for AI Economics

  • Information-as-governance: AI reduces information frictions and ambiguity, improving the accuracy and credibility of corporate non-financial reporting — a clear case of digital technologies producing positive governance externalities.
  • Complementarity and returns to AI: The stronger effects in R&D‑experienced, reputable, and well‑governed firms highlight that returns to AI for governance depend on complementary firm capabilities and institutions.
  • Market and regulatory implications: AI can lower incidences of informational fraud, potentially affecting capital allocation, investor confidence, and the effectiveness of climate policy; regulators could promote AI tools for disclosure verification and monitoring.
  • Policy design: Encouraging AI adoption (and supporting organizational capabilities) may be an efficient policy lever to tighten ESG accountability without solely relying on command-and-control enforcement.
  • Research agenda: Points to broader questions in AI economics about how digital adoption alters incentive structures, monitoring equilibria, and the distribution of benefits across firms and industries.

Assessment

Paper Typecorrelational Evidence Strengthmedium — Large panel of listed firms and multiple complementary mediators and heterogeneity tests strengthen plausibility, but causal interpretation is limited by observational variation in AI adoption and potential time-varying confounders, measurement error in AI adoption and ESG decoupling, and possible reverse causality. Methods Rigormedium — The paper is well‑motivated theoretically (Fraud Triangle) and applies mediation analysis and heterogeneity tests, which are appropriate for mechanism exploration; however, the identification strategy relies on non‑experimental variation without a clearly exogenous shock, instrument, or discontinuity, leaving open confounding and selection concerns that weaken causal claims. SamplePanel of Chinese A‑share listed firms, 2009–2024 (firm-year observations); key variables include firm AI adoption indicator(s), measures of ESG decoupling (gap between ESG disclosures/ratings and observable ESG performance), mediators (environmental uncertainty/ambiguity metrics, media/analyst attention, disclosure quality measures), and firm/industry controls for heterogeneity analyses. Themesgovernance adoption IdentificationUses firm-level variation in AI adoption among Chinese A-share listed firms (2009–2024) and panel regressions with controls; estimates total effect of AI adoption on measured ESG decoupling and applies mediation analysis to decompose effects into three channels (reduced environmental uncertainty, greater external scrutiny, improved disclosure); conducts heterogeneity tests by firm characteristics. No clearly exogenous instrument or natural experiment is reported in the supplied text. GeneralizabilityChina-specific institutional, regulatory, and capital‑market context may limit transferability to other countries, Sample restricted to publicly listed A‑share firms — excludes private firms and small- or medium-sized enterprises, AI adoption measurement and ESG decoupling metrics may be country- and data-source-specific and subject to measurement error, Findings may depend on period (2009–2024) during which AI diffusion and ESG standards evolved rapidly, Observational design raises concern that unobserved firm-level trends or policy changes drive both AI adoption and governance improvements

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI adoption is associated with significantly lower ESG decoupling among Chinese A-share listed firms during 2009–2024. Governance And Regulation negative ESG decoupling between firms’ ESG disclosures and underlying ESG performance
Reading fidelity high
Study strength medium
not reported
0.3
Reduced environmental uncertainty or ambiguity is a mediating pathway through which AI adoption lowers ESG decoupling. Governance And Regulation negative ESG decoupling mediated by environmental uncertainty or ambiguity
Reading fidelity high
Study strength medium
not reported
0.3
AI adoption increases external monitoring through greater media scrutiny and analyst attention, which is associated with lower ESG decoupling. Governance And Regulation negative ESG decoupling under increased media and analyst scrutiny
Reading fidelity high
Study strength medium
not reported
0.3
AI adoption improves disclosure quality and transparency, which is associated with reduced ESG decoupling. Governance And Regulation negative ESG decoupling mediated by disclosure quality and transparency
Reading fidelity high
Study strength medium
not reported
0.3
The negative association between AI adoption and ESG decoupling is stronger among firms whose executives have R&D experience. Governance And Regulation negative ESG decoupling
Reading fidelity high
Study strength medium
not reported
0.3
The negative association between AI adoption and ESG decoupling is stronger among firms with higher corporate reputation. Governance And Regulation negative ESG decoupling
Reading fidelity high
Study strength medium
not reported
0.3
The negative association between AI adoption and ESG decoupling is stronger in firms with stronger internal governance. Governance And Regulation negative ESG decoupling
Reading fidelity high
Study strength medium
not reported
0.3
The negative association between AI adoption and ESG decoupling is stronger for firms operating in highly competitive industries. Governance And Regulation negative ESG decoupling
Reading fidelity high
Study strength medium
not reported
0.3

Notes