Artificial intelligence (AI) is increasingly viewed as a key driver of sustainable development, yet evidence on its role in corporate sustainable green innovation remains limited. Drawing on Resource Orchestration Theory and Signaling Theory, this study examines the impact of AI capability on sustainable green innovation using panel data from Chinese A-share listed companies during 2014–2023. The results show that stronger AI capability significantly promotes sustainable green innovation. AI investment serves as a mediating mechanism. However, the estimated indirect effect is negative, suggesting that the process of AI implementation may involve resource reallocation and organizational adjustment costs before innovation benefits can be fully realized. Environmental investment strengthens the positive impact of AI capability, whereas digital transformation weakens it. Robustness tests confirm the reliability of the findings. Further analyses indicate that the positive effect of AI capability is more pronounced in non-state-owned enterprises, low-technology firms, and firms in the growth stage. By revealing the mechanisms and boundary conditions through which AI capability influences sustainable green innovation, this study enriches the literature on AI-enabled sustainability and offers practical insights for firms pursuing long-term sustainable development.
Micro-level evidence is provided on AI-driven green transformation in manufacturing enterprises from the dynamic capabilities perspective, offering theoretical and practical insights for advancing high-quality transformation of China’s manufacturing sector in the digital-intelligent era.
The research findings show that at the initial application stage, artificial intelligence can facilitate both substantive and strategic green innovation simultaneously, and such impacts present significant heterogeneity across industries and enterprise property rights.
The results show that AI adoption significantly enhances DGS in manufacturing firms, with stronger impacts in high-tech, non-heavy-polluting, and non-state-owned enterprises.
Xiao Tang, Guizhu Tan, Da Gao et al.· Sustainability· 0 citations
The Artificial Intelligence Pilot Zone Policy is an important strategic initiative for building artificial intelligence (AI) innovation hubs. It provides new opportunities to enhance manufacturing firm resilience and promote the sustainable development of the manufacturing sector. The creation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones (AI Pilot Zones) is viewed in this study as a quasi-natural experiment. Using data from Chinese A-share-listed manufacturing firms from 2015 to 2023, we employ a staggered DID model to evaluate the impact of the policy on manufacturing firm resilience. We find that the AI Pilot Zone policy increases manufacturing firm resilience by an average of 0.0282 units. The analysis of potential mechanisms shows that the policy significantly promotes digital talent agglomeration, stimulates urban innovation vitality, and improves firm-level supply chain efficiency. These findings are consistent with the theoretical expectations and provide supportive evidence that these factors may constitute potential mechanisms associated with the policy’s effect on manufacturing firm resilience. The heterogeneity analysis reveals a pronounced “weakness-compensating” effect. At the regional level, the resilience-enhancing effect is stronger for manufacturing firms located in areas with relatively weak digital infrastructure. At the industry level, the effect is more pronounced among firms in low-technology manufacturing industries. At the firm level, the effect is stronger for firms with lower levels of human capital, weaker innovation capacity, and lagging digital transformation. Overall, this study provides micro-level evidence on the resilience effects of the AI Pilot Zone policy and offers policy implications for integrating AI more effectively with the real economy.
Angang Gao, Hong-Jie Lu, Bo Qin· Sustainability· 0 citations
Given the growing ecological and economic risks posed by biodiversity loss, this study uses panel data on Chinese A-share listed firms from 2010 to 2023 and employs a debiased machine learning approach to empirically investigate the impact of artificial intelligence (AI) adoption on corporate biodiversity concern (CBC) and its underlying mechanisms. The results indicate that AI adoption significantly increases CBC. Mechanism analysis indicates that AI adoption enhances CBC by strengthening green knowledge management capabilities, improving green innovation capability, and increasing analyst coverage. Further analysis of moderating effects shows that the positive impact of AI adoption on CBC is more pronounced for firms facing higher climate risk, in regions with stronger environmental regulatory intensity, and among firms with higher levels of digital transformation. This study provides empirical evidence on the role of digital technologies in advancing corporate biodiversity governance.
A framework in which AI enhances green transformation performance through improved operational efficiency, contingent on the dual moderating mechanisms of organizational agility and executives’ environmental attention is proposed, contingent on the dual moderating mechanisms of organizational agility and executives’ environmental attention.
