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Chinese listed manufacturers that adopt AI register higher rates of green product and process innovation, with the boost largest for product innovation; CEO turnover blunts product innovation gains while strengthening process innovation effects, and greater market competition amplifies AI’s positive link to both types of green innovation.

Navigating the Green Innovation Path: The Role of AI Adoption in Green Product and Green Process Innovation
Weiwei Wu, X G Wang · July 15, 2026 · Systems
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Using 2016–2023 panel data on Chinese A-share manufacturing firms, the study finds that AI adoption is positively associated with both green product and process innovation—more strongly for product innovation—with effects that vary by CEO turnover, market competition, firm tech-intensity, and size.

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In response to intensifying environmental pressures and the rapid pace of digital transformation, firms are increasingly turning to artificial intelligence (AI) as a tool to support sustainable development. Using panel data from Chinese A-share-listed manufacturing firms from 2016 to 2023, this study examines the relationship between AI adoption and two forms of green innovation: green product innovation and green process innovation. The results reveal that AI adoption is positively associated with both forms of green innovation, with a stronger association observed for green product innovation. The relationship between AI adoption and green innovation also varies across organizational and market contexts. CEO turnover weakens the association between AI adoption and green product innovation but strengthens its association with green process innovation. Market competition further strengthens the positive association between AI adoption and both types of green innovation. Further heterogeneity tests indicate that these associations tend to be more pronounced among high-tech firms and smaller firms. This study provides new evidence on the relationship between AI adoption and different forms of green innovation. It further clarifies the organizational and market conditions under which AI is more closely linked to corporate green transformation.

Summary

Main Finding

AI adoption by Chinese A-share-listed manufacturing firms (2016–2023) is positively associated with both green product innovation and green process innovation, with a stronger association for green product innovation. The strength of these associations depends on organizational (CEO turnover) and market (competition) contexts and is more pronounced in high-tech and smaller firms.

Key Points

  • Positive association: Firms that adopt AI show higher levels of both green product and green process innovation.
  • Stronger for products: The AI–green innovation link is stronger for green product innovation than for green process innovation.
  • CEO turnover:
    • Weakens the AI → green product innovation relationship.
    • Strengthens the AI → green process innovation relationship.
  • Market competition: Higher competitive pressure amplifies the positive association between AI adoption and both types of green innovation.
  • Heterogeneity: The positive associations are larger among high‑tech firms and among smaller firms.

Data & Methods

  • Sample: Panel data of Chinese A-share-listed manufacturing firms, 2016–2023.
  • Main variables:
    • AI adoption indicator (firm-level).
    • Outcomes: green product innovation and green process innovation (firm-level measures).
    • Moderators: CEO turnover (organizational change) and market competition.
    • Firm characteristics for heterogeneity tests: industry tech-intensity (high-tech vs. others), firm size.
  • Empirical approach (as reported):
    • Panel regression framework exploiting the 2016–2023 panel.
    • Interaction terms to test moderating roles of CEO turnover and market competition.
    • Heterogeneity tests by industry technology level and firm size.
    • (Study reports robustness analyses across specifications; exact estimation details and robustness checks are in the full paper.)

Implications for AI Economics

  • AI as an environmental innovation lever: The findings support the view of AI as a general-purpose technology that can promote firm-level green transformation, particularly for product-related environmental innovations.
  • Organizational context matters: Managerial stability can condition how AI affects different innovation types. CEO turnover disrupts the translation of AI into product innovation but may accelerate AI-driven process changes—implying different coordination and knowledge-reuse demands for product vs. process innovations.
  • Market forces amplify AI effects: Competitive pressure increases firms’ incentives to leverage AI for green innovation, suggesting market structure and competition policy shape the environmental returns to AI investments.
  • Targeting investment and policy:
    • Policies encouraging AI adoption in high‑tech and smaller firms may yield larger green-innovation returns.
    • Encouraging competitive markets and reducing barriers to AI investments could strengthen AI’s environmental impact.
    • Attention to managerial continuity and change management may be needed to realize AI’s full potential for product innovation.
  • Directions for further economic research: identify causal mechanisms (cost-reduction vs. knowledge complementarity), generalize findings beyond listed Chinese manufacturers, refine measurement of AI adoption intensity, and explore long-run productivity–environment trade-offs.

Assessment

Paper Typecorrelational Evidence Strengthlow — The paper documents robust associations in panel data, but it does not present a clear exogenous source of variation in AI adoption (no randomized treatment, natural experiment, or instrumental variable reported in the summary), leaving results vulnerable to omitted variable bias, reverse causality (innovative firms self-selecting into AI), and measurement error in AI adoption and green-innovation proxies. Methods Rigormedium — Use of multi-year firm-level panel data, subgroup and heterogeneity analyses (CEO turnover, market competition, firm tech intensity, firm size) and robustness checks increase credibility, but without a transparent identification strategy (e.g., IV, diff-in-diff with plausibly exogenous timing, or discontinuities) the design cannot reliably support causal claims; further information on controls, fixed effects, and measurement would be needed to upgrade the rating. SampleFirm-year panel of Chinese A-share-listed manufacturing firms spanning 2016–2023; firm-level measures of AI adoption and two forms of green innovation (green product and green process) are used along with firm characteristics (CEO turnover, market competition, technology intensity, firm size); the summary does not report sample size or precise variable construction. Themesinnovation adoption org_design IdentificationObservational panel regressions using firm-year data (2016–2023) to relate firm-level measures of AI adoption to proxies for green product and green process innovation; identification appears to rely on covariate adjustment, panel variation and heterogeneity/robustness checks rather than a quasi-experimental source of exogenous variation. GeneralizabilityLimited to publicly listed manufacturing firms in China (A-share), so findings may not generalize to private firms, SMEs outside listings, non-manufacturing sectors, or other countries., Study period 2016–2023 may capture a particular stage of AI diffusion and Chinese regulatory/market conditions that differ elsewhere or in other periods., Measurement of 'AI adoption' and 'green innovation' may be context- or data-specific (e.g., patent- or disclosure-based proxies) and not fully comparable across settings., Potential selection bias: listed firms that adopt AI may differ systematically from non-adopters in unobserved ways (resources, management quality).

Claims (10)

ClaimDirectionOutcomeConfidence & EvidenceDetails
The study uses panel data from Chinese A-share-listed manufacturing firms from 2016 to 2023. Other null_result sample and timeframe
Reading fidelity high
Study strength high
not reported
0.5
AI adoption is positively associated with green product innovation. Innovation Output positive green product innovation
Reading fidelity high
Study strength medium
not reported
0.3
AI adoption is positively associated with green process innovation. Innovation Output positive green process innovation
Reading fidelity high
Study strength medium
not reported
0.3
The positive association between AI adoption and green innovation is stronger for green product innovation than for green process innovation. Innovation Output positive relative strength of association (product vs process)
Reading fidelity high
Study strength medium
not reported
0.3
CEO turnover weakens the association between AI adoption and green product innovation. Innovation Output negative green product innovation (interaction with CEO turnover)
Reading fidelity high
Study strength medium
not reported
0.3
CEO turnover strengthens the association between AI adoption and green process innovation. Innovation Output positive green process innovation (interaction with CEO turnover)
Reading fidelity high
Study strength medium
not reported
0.3
Market competition strengthens the positive association between AI adoption and green product innovation. Innovation Output positive green product innovation (interaction with market competition)
Reading fidelity high
Study strength medium
not reported
0.3
Market competition strengthens the positive association between AI adoption and green process innovation. Innovation Output positive green process innovation (interaction with market competition)
Reading fidelity high
Study strength medium
not reported
0.3
The positive associations between AI adoption and both types of green innovation are more pronounced among high‑tech firms. Innovation Output positive green product and process innovation (heterogeneity by high‑tech status)
Reading fidelity high
Study strength medium
not reported
0.3
The positive associations between AI adoption and both types of green innovation are more pronounced among smaller firms. Innovation Output positive green product and process innovation (heterogeneity by firm size)
Reading fidelity high
Study strength medium
not reported
0.3

Notes