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View corpus contextAI development is linked to stronger green value co-creation among Chinese manufacturers by boosting technological spillovers and productivity; financing constraints blunt the effect while corporate influence and state ownership amplify it.
The Impact of Artificial Intelligence on Corporate Green Value Co-Creation: Empirical Evidence from China’s Manufacturing Industry
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correlational
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In a panel of Chinese listed manufacturing firms (2015–2024), greater AI development is associated with increased green value co-creation, acting via enhanced technological spillovers and higher TFP, with financing constraints weakening and corporate influence strengthening the relationship and larger effects for SOEs, high-pollution firms, and firms under voluntary regulation.
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Against the dual demands of green transformation and digital integration in the manufacturing industry, green value co-creation has become a core pathway for enterprises to achieve sustainable development. However, the role of artificial intelligence (AI) in driving green value co-creation remains under explored, especially in the context of Chinese manufacturing. To enrich this research, this study aims to investigate the impact of AI development on corporate green value co-creation and its intrinsic mechanism. This study draws on panel data of listed manufacturing enterprises listed on China’s Shanghai and Shenzhen A share markets spanning the period 2015–2024, and employs multiple regression and negative binomial regression as research methodologies to empirically examine the impact of AI development on corporate green value co-creation and its underlying mechanisms. The results demonstrate that: AI development exerts a significantly positive effect on manufacturing enterprises’ green value co-creation, which is achieved by enhancing firms’ technological spillover capacity and total factor productivity (TFP); financing constraints negatively moderate the aforementioned relationship, while corporate influence plays a positive moderating role; heterogeneity analysis reveals that this impact is more pronounced for enterprises under voluntary regulation, state-owned enterprises (SOEs), and high-pollution enterprises. This study elucidates AI’s role and mechanism in corporate green development at the micro level, provides empirical evidence for related research, and offers practical insights to promote enterprise AI advancement and green value co-creation.
Summary
Main Finding
AI development significantly increases green value co-creation by Chinese manufacturing firms (2015–2024). The effect operates through two mediating channels — enhanced technological spillover capacity and higher total factor productivity (TFP) — and is moderated negatively by financing constraints and positively by corporate influence. The positive impact is stronger for firms under voluntary regulation, state-owned enterprises (SOEs), and high-pollution firms.
Key Points
- Sample and scope: Panel of manufacturing firms listed on Shanghai and Shenzhen A-share markets, 2015–2024.
- Primary result: Firm-level AI development → higher corporate green value co-creation (statistically significant).
- Mechanisms (mediators):
- Technological spillover capacity: AI increases firms’ ability to transfer and diffuse technologies, facilitating collaborative green actions.
- Total factor productivity (TFP): AI raises efficiency/productivity, enabling more resources and capability for green co-creation.
- Moderators:
- Financing constraints weaken the AI → green co-creation effect.
- Corporate influence (firm stature/market/political influence) strengthens the effect.
- Heterogeneity: The positive impact is more pronounced for firms that are (a) subject to voluntary regulation, (b) state-owned enterprises, and (c) in high-pollution industries.
Data & Methods
- Data: Panel dataset of manufacturing firms listed on Shanghai and Shenzhen A-share exchanges covering 2015–2024.
- Empirical strategy:
- Baseline: Multiple regression analyses to estimate the relationship between AI development and green value co-creation.
- Robustness/count data: Negative binomial regression used where outcome counts or over-dispersed discrete outcomes are involved.
- Mediation tests: Empirical checks showing technological spillovers and TFP transmit the effect of AI to green co-creation.
- Moderation and heterogeneity analyses: Interaction terms and subgroup regressions to assess financing constraints, corporate influence, regulation type, ownership, and pollution intensity.
- (Notes) Specific variable operationalizations, identification strategies, and robustness checks are not detailed in the summary provided.
Implications for AI Economics
- Microeconomic role of AI: Provides empirical micro-level evidence that firm AI capabilities contribute to sustainable outcomes beyond productivity gains — specifically, enabling collaborative green value creation.
- Policy levers:
- Promote AI adoption targeted at green-tech applications and inter-firm technology diffusion to amplify green co-creation.
- Alleviate financing constraints (e.g., access to green/innovation finance, credit supports) to unlock the full sustainability benefits of AI.
- Leverage corporate influence and public–private partnerships to scale green co-creation initiatives, especially for firms with stronger market or political capital.
- Use regulatory design: voluntary regulation and incentives can intensify AI’s positive environmental effects; tailor policies to industry pollution intensity and ownership types.
- Managerial implications:
- Firms should invest in AI capabilities that enhance spillovers and productivity while pursuing governance and influence strategies to maximize green outcomes.
- SOEs and high-pollution firms represent priority targets for policy and firm-level interventions to accelerate green co-creation.
- Directions for further research:
- Clarify causal identification (e.g., IVs, natural experiments) to strengthen causal claims about AI → green co-creation.
- Disaggregate AI by type/use case (e.g., process automation vs. environmental monitoring) to pinpoint which AI applications drive green co-creation.
- Explore long-term dynamics, cross-country generalizability, and firm-level complementarities (human capital, digital infrastructure) that condition the AI–green relationship.
Assessment
Paper Typecorrelational
Evidence Strengthmedium — Uses a large panel of listed manufacturing firms across 2015–2024 and tests mechanisms and heterogeneity, providing plausible associative evidence; however, causal claims are limited by observational design, potential endogeneity (reverse causality, omitted variables), and unclear measurement validity for AI development.
Methods Rigormedium — Appropriate econometric choices for panel data (regressions and negative binomial where outcome is count-like), plus mediation and moderation analyses and heterogeneity checks; but the description lacks details on identification safeguards (fixed effects, lagging, instruments, robustness to alternative specifications), clustering, and measurement construction, leaving important threats to inference unaddressed.
SamplePanel of manufacturing firms listed on China’s Shanghai and Shenzhen A-share markets, 2015–2024; firm-level variables include an AI development measure (unspecified proxy), a green value co-creation outcome (count-like), technological spillover measures, total factor productivity (TFP), financing constraint indicators, corporate influence, and firm controls; sample restricted to publicly listed manufacturing firms (sample size not stated).
Themesinnovation productivity adoption
IdentificationObservational panel regressions (multiple regression and negative binomial models) relating firm-level measures of AI development to a green value co-creation outcome, with mediation tests (technological spillovers, TFP) and moderation analyses (financing constraints, corporate influence); no explicit exogenous variation or instrumental variables reported.
GeneralizabilityChina-only context — institutional, regulatory, and market features may not generalize to other countries, Only listed manufacturing firms — excludes SMEs and unlisted/private firms, biasing toward larger, more resource-rich firms, Manufacturing sector focus — findings may not apply to services or other industries, Observational design and potential measurement issues for AI development limit causal generalization to other settings, Time window 2015–2024 — rapid AI evolution may change effects outside this period
Claims (9)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| AI development exerts a significantly positive effect on manufacturing enterprises’ green value co-creation. Innovation Output | positive | green value co-creation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| The positive effect of AI development on corporate green value co-creation is achieved by enhancing firms’ technological spillover capacity. Innovation Output | positive | green value co-creation (mediated by technological spillover capacity) |
Reading fidelity
high
Study strength
medium
|
not reported
|
| The positive effect of AI development on corporate green value co-creation is achieved by enhancing firms’ total factor productivity (TFP). Innovation Output | positive | green value co-creation (mediated by total factor productivity) |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Financing constraints negatively moderate the relationship between AI development and corporate green value co-creation (i.e., financing constraints weaken AI's positive effect). Innovation Output | negative | green value co-creation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Corporate influence positively moderates the relationship between AI development and corporate green value co-creation (i.e., corporate influence strengthens AI's positive effect). Innovation Output | positive | green value co-creation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| The positive impact of AI development on corporate green value co-creation is more pronounced for enterprises under voluntary regulation. Innovation Output | positive | green value co-creation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| The positive impact of AI development on corporate green value co-creation is more pronounced for state-owned enterprises (SOEs). Innovation Output | positive | green value co-creation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| The positive impact of AI development on corporate green value co-creation is more pronounced for high-pollution enterprises. Innovation Output | positive | green value co-creation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| This study uses panel data of listed manufacturing enterprises on China's Shanghai and Shenzhen A-share markets (2015–2024) and employs multiple regression and negative binomial regression for empirical examination. Other | null_result | research methodology / data description |
Reading fidelity
high
Study strength
high
|
not reported
|