Human-AI Technology Integration and Green ESG Performance: Evidence from Chinese Retail Enterprises

📅 2025-07-03
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This study investigates the impact mechanism of human–AI collaborative intelligence integration on retail firms’ green ESG performance. Using annual observations from 5,400 Chinese listed firms (2019–2023) drawn from CNRDS and CSMAR databases, we employ fixed-effects regression and mediation analysis. Results show that AI integration significantly enhances green ESG performance, with approximately 35% of this effect mediated by green technological innovation. Heterogeneity analyses reveal stronger effects among large firms, state-owned enterprises, and firms located in eastern, more developed regions. This research is the first to systematically identify a dual-path mechanism—“technology-driven” and “governance-enhanced”—through which AI enables sustainable development. By bridging AI adoption with ESG outcomes, it extends theoretical understanding of ESG drivers and provides empirical evidence and policy implications for aligning digital transformation with green performance in emerging markets.

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📝 Abstract
This study examines the relationship between human-AI technology integration transformation and green Environmental, Social, and Governance (ESG) performance in Chinese retail enterprises, with green technology innovation serving as a mediating mechanism. Using panel data comprising 5,400 firm-year observations from 2019 to 2023, sourced from CNRDS and CSMAR databases, we employ fixed-effects regression models to investigate this relationship. Our findings reveal that human-AI technology integration significantly enhances green ESG performance, with green technology innovation serving as a crucial mediating pathway. The results demonstrate that a one standard-deviation increase in human-AI integration leads to a 12.7% improvement in green ESG scores. The mediation analysis confirms that approximately 35% of this effect operates through enhanced green technology innovation capabilities. Heterogeneity analysis reveals stronger effects among larger firms, state-owned enterprises, and companies in developed regions. These findings contribute to the growing literature on digital transformation and sustainability by providing empirical evidence of the mechanisms through which AI integration drives environmental performance improvements in emerging markets.
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Research questions and friction points this paper is trying to address.

Examining human-AI integration impact on green ESG performance
Analyzing green tech innovation as a mediating mechanism
Assessing effect variations across firm types and regions
Innovation

Methods, ideas, or system contributions that make the work stand out.

Human-AI integration boosts green ESG performance
Green technology innovation mediates AI-ESG link
Fixed-effects models analyze panel data effects
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J
Jun Cui
Solbridge International School of Business, Woosong University, Daejeon, Republic of Korea