In emerging Asia, firms with stronger AI capabilities produce greener innovations more efficiently, but the environmental payoff is substantially larger when firms have robust ESG practices; AI alone is insufficient without governance that fosters transparency and accountability.

Artificial Intelligence, ESG Governance, and Green Innovation Efficiency in Emerging Economies

Marwan Mansour, Mo’taz Al Zobi, Mohammed Alomair · December 31, 2025 · Economies

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Firm-level AI capability is positively associated with higher green innovation efficiency across 4,926 publicly listed firms in 15 emerging Asian economies, and this effect is strengthened in firms with better ESG performance.

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Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies.

Summary

Main Finding

AI capability is positively and significantly associated with higher green innovation efficiency (GIE) among publicly listed firms in emerging Asian economies, and this positive effect is strengthened when firms exhibit stronger ESG performance. In other words, AI adoption yields greater environmental innovation benefits where complementary governance, transparency, and risk-management practices are in place.

Key Points

Sample: 59,112 firm-year observations from 4,926 publicly listed firms across 15 emerging Asian economies (2011–2022).
Core result: Firm-level AI capability → higher GIE (statistically significant).
Moderation: ESG performance amplifies the AI → GIE relationship (positive interaction).
Robustness: Findings hold after addressing unobserved heterogeneity, dynamic effects, endogeneity, self-selection, alternative model specifications, and sensitivity/heterogeneity analyses.
Interpretation: AI adoption alone is insufficient for sustainable outcomes; institutional and governance complementarities (ESG) are critical to realize environmental returns to digital transformation.

Data & Methods

Data scope: Large-scale, multi-country, unbalanced panel of publicly listed firms in emerging Asia spanning 2011–2022.
Key variables: firm-level measures of AI capability, green innovation efficiency (GIE), and ESG performance (details not specified in summary).
Econometric approach: comprehensive panel-data framework designed to:
- Control for unobserved firm and country heterogeneity,
- Capture dynamic effects (e.g., lagged outcomes or adjustment processes),
- Address endogeneity concerns,
- Correct for potential self-selection bias.
Validation: Extensive robustness checks and heterogeneity analyses to confirm stability of results across specifications and subsamples.

Implications for AI Economics

For policy:
- Encourage ESG-enhancing reforms (disclosure standards, corporate governance, accountability mechanisms) to unlock the environmental benefits of AI.
- Design digitalization policies that pair AI adoption incentives with governance and sustainability conditions.
- Support complementary public infrastructure (data governance, skilled labor) that helps firms deploy AI for green innovation.
For firm strategy:
- Combine AI investments with investments in ESG practices (transparency, stakeholder engagement, risk management) to maximize sustainability returns.
- Treat governance and sustainability frameworks as complements, not afterthoughts, when planning digital transformation.
For research:
- Investigate micro-level mechanisms linking AI capability and GIE (e.g., R&D reallocation, process optimization, green product design).
- Disaggregate ESG into environmental, social, and governance components to identify which elements most strongly moderate AI’s effect.
- Extend analysis to non-listed firms, other regions, and longer horizons; pursue causal identification strategies to strengthen inference.
Broader takeaway:
- The sustainability value of AI depends on institutional complements; understanding these complementarities is crucial for assessing the net social and environmental impacts of digitalization in emerging economies.

Assessment

Paper Typequasi_experimental Evidence Strengthmedium — Large-scale, multi-country firm panel (59,112 firm-year observations) and multiple econometric corrections (fixed effects, dynamic estimators, IV/selection corrections, robustness checks) strengthen confidence in the association; however the design remains observational so residual endogeneity, measurement error in AI capability/green-innovation metrics and ESG, and potential omitted variables or reverse causality cannot be fully ruled out. Methods Rigormedium — The authors implement a comprehensive set of modern panel techniques that address key econometric concerns (heterogeneity, dynamics, endogeneity, selection) and report extensive sensitivity analyses; nonetheless, without a clear exogenous shock, randomized assignment, or fully convincing instruments documented here, causal claims remain subject to the usual limits of observational inference and construct validity. SampleUnbalanced panel of 59,112 firm-year observations from 4,926 publicly listed firms across 15 emerging Asian economies spanning 2011–2022; firm-level measures of AI capability, green innovation efficiency, and ESG performance used in econometric analyses. Themesinnovation governance adoption IdentificationObservational panel-data identification using an unbalanced firm-year panel with firm and time heterogeneity controlled; dynamic panel estimators to account for persistence (e.g., lagged dependent variables); corrections for endogeneity (instrumental variables or similar IV approaches) and for self-selection (propensity-score/selection-correction or Heckman-type methods); country and year controls and extensive robustness and sensitivity checks across alternative specifications. GeneralizabilityLimited to publicly listed firms (excludes private and informal firms that dominate some emerging markets), Restricted to 15 emerging Asian economies—results may not generalize to advanced economies or to other regions, Time period 2011–2022 may miss more recent rapid developments in AI adoption and regulation, Measurement and reporting heterogeneity of AI capability, green innovation outcomes, and ESG scores across countries can bias cross-country comparisons, Sectoral composition of the sample (e.g., manufacturing vs services) may affect the applicability to all industries

Claims (7)

Claim	Direction	Outcome	Confidence & Evidence	Details
The study uses an unbalanced panel of 59,112 firm-year observations from 4,926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022. Other	null_result	sample coverage (number of firms, firm-year observations, countries, years)	Reading fidelity high Study strength high	n=59112 0.8
AI capability is positively and significantly associated with higher green innovation efficiency (GIE). Research Productivity	positive	green innovation efficiency (GIE)	Reading fidelity high Study strength medium	n=59112 0.48
ESG performance strengthens (positively moderates) the relationship between AI capability and green innovation efficiency. Research Productivity	positive	moderation effect of ESG on the AI -> green innovation efficiency relationship	Reading fidelity high Study strength medium	n=59112 0.48
AI adoption alone is insufficient to generate sustainable value; its environmental effectiveness depends critically on complementary governance structures (ESG) that promote transparency, accountability, and responsible risk management. Governance And Regulation	mixed	conditional effectiveness of AI adoption for sustainable/environmental outcomes	Reading fidelity high Study strength medium	n=59112 0.48
The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Research Productivity	null_result	robustness of AI -> green innovation efficiency and its moderation by ESG	Reading fidelity high Study strength medium	n=59112 0.48
The empirical strategy employs a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. Other	null_result	use of specific econometric methods (controls for heterogeneity, dynamics, endogeneity, self-selection)	Reading fidelity high Study strength high	n=59112 0.8
This study provides large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies. Research Productivity	positive	evidence on ESG's role in moderating AI's impact on green innovation efficiency across multiple countries	Reading fidelity high Study strength medium	n=59112 0.48