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Green spending by polluting Chinese firms is linked to higher stock returns, driven in part by green patents; firms that adopt AI see an even stronger market reward.

Green investment and stock returns of heavily polluting firms in China: Does AI adoption and green technological innovation matter?
Hina Qayum, Talal H. Alsabhan, Yaya Li, Muhammad Anas · Fetched July 20, 2026 · Frontiers in Environmental Science
semantic_scholar correlational low evidence 7/10 relevance Full text usable extracted full text DOI Source PDF

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In a panel of Chinese heavy-polluting firms (2012–2023), higher green investment is associated with higher stock returns, this relationship is partly mediated by green patenting, and strengthened where firms have adopted AI.

Citation observations

Cumulative provider counts captured on specific dates; providers are never combined.

Green investment plays a significant role in the firms’ financial and environmental performance. While the positive environmental impacts of green investment are known, its economic outcomes from the investors’ perspectives have mainly remained unexamined, specifically in the AI supported environment. Accordingly, this study examines the impact of green investment on stock returns, with green technological innovation (G_patent)) mediating this relationship. The study also examines the moderating role of AI adoption. The data for testing the proposed framework was collected from heavily polluting firms in China for the period 2012-2023. The results revealed that green investment had a direct effect on the firm’s stock return. Furthermore, green technological innovation mediated the relationship between green investment and stock returns. The results also confirmed the moderating role of AI adoption, revealing that AI adoption strengthened the direct impact of green investment on stock returns. The study contributes to the literature by revealing how green investment can increase the investors’ worth in the form of stock return, an area that has been underexamined.

Summary

Main Finding

Green investment by Chinese heavily polluting firms (A‑share listed) from 2012–2023 is associated with higher stock returns. That positive effect operates partly through green technological innovation (measured via green patents) and is strengthened when firms adopt AI — i.e., AI adoption moderates (amplifies) the direct green‑investment → stock‑return link.

Key Points

  • Theory: Framed using the Resource‑Based View (RBV) — green investment builds firm‑specific, hard‑to‑imitate resources (green technological capabilities) that create investor value.
  • Direct effect: Green investment positively predicts firms’ stock returns (H1).
  • Mediation: Green technological innovation (G_patent) mediates the relationship, linking green investment to improved investor outcomes (H3).
  • Moderation: AI adoption strengthens the direct economic payoff of green investment on stock returns (H2) by improving project selection, operational efficiency, transparency, and risk control.
  • Contribution: Resolves some inconsistent prior findings by showing green investment need not trade off with shareholder value when combined with green technological innovation and AI.
  • Sector focus: Results are specific to heavily polluting Chinese industries (e.g., non‑metallic mineral products, metal products, equipment manufacturing, textiles, rubber/plastics).

Data & Methods

  • Sample: Chinese A‑share listed heavily polluting firms, 2012–2023. Firms classified per China Securities Regulatory Commission industry guidelines.
  • Data sources: China Stock Market & Accounting Research (CSMAR) for green investment, AI adoption, stock returns, and controls; China Research Data Service Platform (CNRDS) for green technological innovation (green patent counts).
  • Exclusions: ST firms and other firms with missing/invalid green‑investment data (paper reports excluding ST, *ST, PT as applicable).
  • Empirical approach: Panel empirical analysis testing (i) direct effect of green investment on stock returns, (ii) mediation by green patents, and (iii) moderation by AI adoption. Robustness checks reported (paper summary indicates multiple tests though specific model forms are not detailed in the excerpt).
  • Identification caveats: Paper reports associations using firm‑level panel data; causality and measurement granularity (especially for AI adoption) may be limited.

Implications for AI Economics

  • Complementarity: AI acts as a multiplier on returns to green capital — a concrete example of technology complementarity where digital adoption raises the productivity and financial payoffs of environmental investments.
  • Investment allocation: Investors and fund managers valuing green strategies should incorporate firms’ AI capabilities (not just green capex or patent counts) into valuation, screening, and risk models.
  • Policy design: Policies that jointly incentivize green investment and AI adoption (e.g., matched subsidies, tax credits for AI‑enabled green projects, clearer disclosure standards for AI use in sustainability) can increase private capital flows into decarbonization.
  • Firm strategy and finance: Firms in high‑pollution sectors can improve investor perceptions and stock performance by coupling green R&D/capex with AI deployment (for forecasting, predictive maintenance, compliance reporting, process optimization).
  • Research directions: Quantifying the marginal return to green investment conditional on AI adoption, mapping heterogeneous AI capabilities (analytics vs. automation vs. NLP) to financial outcomes, and causal identification (instrumental variables, natural experiments) to confirm mechanisms are promising next steps.

Assessment

Paper Typecorrelational Evidence Strengthlow — The paper reports associations from observational firm-level data without a clear strategy to address endogeneity (reverse causality, omitted heterogeneity, or simultaneous investment and returns decisions). Mediation and moderation analyses on panel data are informative but do not by themselves establish causal effects on stock returns. Methods Rigormedium — Using firm-level panel data over 2012–2023 and testing mediated and moderated relationships is a reasonably rigorous correlational approach; however, the absence of explicit identification strategies (e.g., instruments, difference-in-differences, event studies, or natural experiments), limited detail on variable construction, and likely measurement error in AI adoption and green investment limit methodological rigor. SampleFirm-level panel of heavily polluting firms in China observed 2012–2023 (details on exact sample size, whether firms are publicly listed, sector breakdown, and sampling/exclusion criteria not provided); variables include measures of green investment, green patents (G_patent), AI adoption, and stock returns. Themesinnovation adoption IdentificationObservational panel analysis relating firm-level green investment to subsequent stock returns, with mediation tested via counts/measures of green patents and moderation by measures of AI adoption; no clear quasi-experimental source of exogenous variation or instrument reported. GeneralizabilityRestricted to heavily polluting Chinese firms — may not generalize to other countries or less-polluting sectors, If sample limited to listed firms, results reflect capital-market contexts specific to listed companies, Findings may be period-specific (2012–2023) and influenced by China-specific green policy and financial market dynamics, Measures of AI adoption and green patents may be noisy or context-dependent, limiting transferability, Correlation-based results may not generalize to causal effects in other institutional environments

Claims (4)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Green investment had a direct effect on the firm’s stock return. Firm Revenue positive stock return
Reading fidelity high
Study strength medium
not reported
0.3
Green technological innovation (G_patent) mediated the relationship between green investment and stock returns. Firm Revenue positive stock return (mediated via green technological innovation / patents)
Reading fidelity high
Study strength medium
not reported
0.3
AI adoption moderated the relationship, strengthening the direct impact of green investment on stock returns. Firm Revenue positive stock return (interaction effect with AI adoption)
Reading fidelity high
Study strength medium
not reported
0.3
The study's data were collected from heavily polluting firms in China for the period 2012–2023. Other null_result data coverage / sample description
Reading fidelity high
Study strength high
not reported
0.5

Notes