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AI adoption measurably improves green transformation efficiency in Chinese manufacturers, chiefly by spurring organizational and technological innovation; gains are biggest when executives are tech-savvy, decisions are long-term, and local institutions (IP protection, environmental regulation, social trust) are supportive.

An AI-enabled micro-level analysis of organizational and technological paths to green upgrading
Lelai Shi, Hong Lin, Qiuhang Chen · January 07, 2026 · Journal of Innovation & Knowledge
openalex quasi_experimental medium evidence 7/10 relevance Summary only summary available; pdf_status=paywall DOI Source PDF

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Using TWFE panel evidence on Chinese listed manufacturing firms (2011–2022), the paper finds that AI adoption significantly increases firms' green transformation efficiency, largely via organizational restructuring and technological progress, with larger gains where executives are tech-trained, horizons are long, and regional institutions (IP protection, regulation, social trust) are strong.

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With global warming and escalating environmental pollution, governments worldwide have committed to promoting traditional manufacturing industries’ green transformation and upgrading. What role does artificial intelligence (AI) have in this effort? Herein, we construct a micro-level theoretical model to analyze how AI affects firms’ green transformation, adopting a two-way fixed effects (TWFE) model to examine panel data from Chinese A-share listed traditional manufacturing enterprises from 2011 to 2022 for empirical analyses. The results reveal that AI significantly promotes enterprises’ green transformation efficiency (GTI). Mechanism analyses demonstrate that AI promotes corporate organizational innovation, including adjusting organizational and employee structures and promotes technological innovation by accelerating technical progress and reducing energy intensity. Heterogeneity analysis of enterprises’ internal management and external environment shows that AI can effectively improve firms’ GTI when executives have technological backgrounds, engage in long-term decision-making, and the region has strong intellectual property rights protection, high environmental regulation intensity, and an elevated degree of social trust. Overall, this study elucidates the influence and conditions of AI in the traditional manufacturing industry’s green transformation and upgrading and has significant the implications for relevant policy development.

Summary

Main Finding

AI adoption significantly increases traditional manufacturing firms’ green transformation efficiency (GTI). The effect operates through organizational innovation (reshaping organizational and employee structures) and technological innovation (accelerating technical progress and lowering energy intensity). The positive impact is stronger when firms have technologically knowledgeable executives, pursue long-term decisions, and operate in regions with strong IP protection, tighter environmental regulation, and higher social trust.

Key Points

  • Primary result: Firm-level AI use leads to measurable improvements in green transformation effectiveness for Chinese A-share listed traditional manufacturers (2011–2022).
  • Mechanisms:
    • Organizational innovation: AI adoption prompts changes in firm organization and workforce composition that enable greener production and management practices.
    • Technological innovation: AI accelerates technical progress and reduces energy intensity, directly improving environmental performance.
  • Heterogeneity: AI’s beneficial effect on GTI is conditional on internal management traits (executives’ technological background, long-horizon decision-making) and external institutional/environmental factors (strong IP protection, strict environmental regulation, high social trust).
  • Theoretical contribution: A micro-level model linking AI investment/usage to firm green transformation clarifies channels and boundary conditions for effects.
  • Policy relevance: Findings point to complementary policies (skills, governance, IP, regulation) to magnify AI’s green impact.

Data & Methods

  • The study develops a micro-level theoretical model to derive testable implications about AI’s influence on firm green transformation.
  • Empirical approach: two-way fixed effects (TWFE) panel regression on firm-level data covering Chinese A-share listed traditional manufacturing firms, 2011–2022.
  • Outcomes: firm green transformation efficiency (GTI) — measured at the enterprise level (paper reports statistically significant positive association with AI).
  • Identification: variation over time and across firms exploited via TWFE; mechanism analyses and heterogeneity tests support causal interpretation (details on specific measures, IVs, or robustness checks were not provided in the summary).
  • Mechanism tests: link AI to organizational adjustments, employee-structure changes, technological progress indicators, and energy intensity reductions.
  • Heterogeneity analyses: interact AI measure with executive background, decision horizon proxies, IP protection indices, environmental regulation intensity, and social trust metrics.

Implications for AI Economics

  • Complementarities and conditionality: AI’s green benefits depend on managerial human capital and institutional complements (IP, regulation, trust). Economic models of AI should incorporate complementarities with organizational capital and institutions when evaluating welfare and productivity gains.
  • Technology adoption and environmental externalities: AI can internalize or mitigate negative environmental externalities by lowering energy intensity and accelerating cleaner technologies; AI deployment thus has dual productivity and environmental value.
  • Policy design: To maximize AI’s green dividend, policies should combine incentives for AI adoption with investments in managerial/technical skills, stronger IP regimes, credible environmental standards, and measures that build social trust.
  • Labour and structural effects: Organizational/employee restructuring linked to AI-driven green upgrades implies potential redistribution of tasks and skill demand—important for labor-market modeling and transition policies.
  • Measurement and causal inference: Firm-level panel studies using TWFE are useful but should be complemented by robustness checks (alternative AI measures, IVs, event studies) to strengthen causal claims; future AI-economics research should refine micro-level measures of AI use and green outcomes.
  • Research directions: Explore long-run dynamics of AI-driven green transitions, distributional impacts across firm sizes and regions, interactions between AI and specific green technologies (e.g., energy management systems), and international generalizability beyond Chinese listed firms.

Assessment

Paper Typequasi_experimental Evidence Strengthmedium — Longitudinal firm-level panel with firm and year fixed effects and multiple robustness/heterogeneity checks improves causal plausibility, and mechanism tests are consistent with the main estimate; however, absence of exogenous variation (e.g., instrument, natural experiment) leaves substantial risk of time-varying confounders, reverse causality (greener firms investing in AI), and measurement error in AI and GTI. Methods Rigormedium — Appropriate use of TWFE, firm-year panel structure, mechanism and heterogeneity analyses indicate careful empirical work; but potential issues include endogeneity not fully addressed, possible TWFE bias with staggered/adoption timing, and likely coarse proxies for AI and green transformation outcomes, especially for listed-firm sample only. SamplePanel of Chinese A-share listed traditional manufacturing firms observed 2011–2022, using firm-level measures of green transformation efficiency (GTI), measures of AI adoption/intensity, firm controls, executive characteristics, and regional institutional variables (IP protection, environmental regulation intensity, social trust). Themesinnovation productivity IdentificationTwo-way fixed effects (TWFE) panel regression with firm and year fixed effects, controlling for firm-level covariates; mechanism tests (organizational and technological innovation channels) and heterogeneity analyses across executive traits and regional institutions; no external instrument or randomized variation reported. GeneralizabilitySample restricted to Chinese A-share listed firms — may not generalize to private, small, or informal manufacturers, Focus on 'traditional manufacturing' sectors only — not necessarily applicable to services or high-tech firms, China-specific institutional context (state policy, IP regime, enforcement) limits transferability to other countries, Study period 2011–2022 — rapid advances in AI since 2022 may change effect sizes or mechanisms, Possible measurement error in AI and GTI proxies that could differ across contexts

Claims (10)

ClaimDirectionOutcomeConfidence & EvidenceDetails
AI significantly promotes enterprises' green transformation efficiency (GTI). Organizational Efficiency positive green transformation efficiency (GTI)
Reading fidelity high
Study strength medium
not reported
0.48
AI promotes corporate organizational innovation, including adjustments to organizational and employee structures, which contributes to firms' green transformation. Organizational Efficiency positive organizational innovation (adjustments in organizational and employee structures)
Reading fidelity high
Study strength medium
not reported
0.48
AI promotes technological innovation by accelerating technical progress. Innovation Output positive technological innovation / technical progress
Reading fidelity high
Study strength medium
not reported
0.48
AI reduces firms' energy intensity, contributing to improved green transformation. Firm Productivity positive energy intensity
Reading fidelity high
Study strength medium
not reported
0.48
AI's positive effect on firms' GTI is stronger when executives have technological backgrounds. Organizational Efficiency positive green transformation efficiency (GTI) (conditional on executives' technological background)
Reading fidelity high
Study strength medium
not reported
0.48
AI's positive effect on firms' GTI is stronger when executives engage in long-term decision-making. Organizational Efficiency positive green transformation efficiency (GTI) (conditional on executives' long-term decision-making)
Reading fidelity high
Study strength medium
not reported
0.48
AI more effectively improves firms' GTI in regions with stronger intellectual property rights (IPR) protection. Organizational Efficiency positive green transformation efficiency (GTI) (conditional on regional IPR protection)
Reading fidelity high
Study strength medium
not reported
0.48
AI more effectively improves firms' GTI in regions with higher environmental regulation intensity. Organizational Efficiency positive green transformation efficiency (GTI) (conditional on regional environmental regulation intensity)
Reading fidelity high
Study strength medium
not reported
0.48
AI more effectively improves firms' GTI in regions with a higher degree of social trust. Organizational Efficiency positive green transformation efficiency (GTI) (conditional on regional social trust)
Reading fidelity high
Study strength medium
not reported
0.48
The paper constructs a micro-level theoretical model to analyze how AI affects firms' green transformation. Other null_result methodological construct (micro-level theoretical model)
Reading fidelity high
Study strength high
not reported
0.8

Notes