ArticleFrontiers in public health2025
Artificial intelligence, green innovation efficiency, and public health benefits: evidence from China's pilot zones with a DID approach.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Introduction: China has established Artificial Intelligence Innovation and Development Pilot Zones (AIDP) to promote industrial upgrading and digital transformation. This study evaluates whether these AI oriented policies enhance firm-level green innovation efficiency (GIE) and generate potential environmental and health benefits. Methods: Using panel data of Chinese firms from 2011 to 2022, we employed a difference-in-differences (DID) design exploiting the staggered implementation of AIDP as a quasi-experiment. The estimation identifies the causal effects of AIDP on green R&D efficiency and green innovation output efficiency, measured by DEA-based indicators and patent productivity. Results: The DID estimates show that AIDP increases firms' green R&D efficiency by approximately 1.7% and green innovation output efficiency by 1.4% relative to non-pilot firms, corresponding to the DID coefficients of 0.017 and 0.014. These effects are primarily driven by enhanced AI hardware investment, improved asset liquidity, and strengthened ESG performance, which together boost firms' digital capability, operational efficiency, and sustainability orientation. Mechanism analysis further indicates that these improvements reduce pollution intensity and support cleaner production processes, suggesting potential environmental and health benefits inferred from pollution reduction. Heterogeneity analysis reveals that the treatment effects are larger among non-state-owned enterprises, manufacturing firms, and labor-intensive sectors, underscoring the importance of tailoring interventions to sectoral and institutional contexts. Conclusion: AIDP generates economically and statistically significant improvements in green innovation efficiency, confirming the potential of AI oriented policies to promote sustainable industrial transformation. By fostering green technologies that cut emissions and reduce industrial pollution, these policies deliver dual dividends: advancing environmental sustainability and supporting public health. The findings highlight the importance of expanding pilot zones, integrating digital initiatives with green finance, and investing in digital infrastructure to maximize policy effectiveness and generate broader environmental and health benefits.
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