How enterprises can leverage frontier technologies to achieve synergy between environmental governance and high-quality development has become a critical issue amid the deepening global push for sustainable development and the green economic transition. Based on micro-level data of Chinese enterprises from 2009 to 2023, this study systematically examines the impact of artificial intelligence (AI) on corporate green governance performance and explores the underlying mechanisms. The findings reveal that AI significantly enhances green governance performance at the enterprise level, and this effect remains robust after accounting for potential endogeneity. Mechanism analysis shows that AI empowers green transformation through a dual-path mechanism of “cognition–behavior,” by strengthening environmental tendency and increasing environmental investment. Further heterogeneity analysis indicates that the positive effects are more pronounced in nonheavy polluting industries and state-owned enterprises, suggesting that industry characteristics and ownership structure moderate the green governance impact of AI. This study contributes to the theoretical foundation of research at the intersection of digital technology and green governance, and provides empirical evidence and policy insights to support AI-driven green transformation in practice.

The corporate maturity mismatch is highly likely to trigger systemic financial risks, which is a realistic issue commonly faced by businesses. In the context of the intelligent era, the impact of artificial intelligence on maturity mismatch has emerged as a focal point of academic inquiry. Leveraging data from Chinese A-share companies over the 2011–2023 timeframe, this research employs a double machine learning approach to systematically examine the influence and underlying mechanisms of artificial intelligence on maturity mismatch. The findings reveal that artificial intelligence significantly exacerbates maturity mismatch. However, this effect is notably mitigated by government subsidies, media attention, and collectivist cultural. Further analysis indicates that in high-climate-risk scenarios, collectivist culture exerts a notably strong moderating influence. By contrast, government subsidies and media attention exhibit stronger moderating influences in low-climate-risk environments. This study constructs a multi-stakeholder collaborative governance framework, which helps to reveal the 'black box' between artificial intelligence and maturity mismatch, thereby offering a theoretical basis for monitoring maturity mismatch.

The impact of Artificial Intelligence (AI) on firm performance is an emerging issue in both practice and research. However, discussions surrounding the effect of AI on productivity are enshrouded in a paradoxical quandary. This study examines the relationship between AI implementation and total factor productivity (TFP), considering the moderation effects of digital infrastructure quality, business diversification, and demand uncertainty. Using data from 2155 Chinese firms over 2016-2021, our empirical analysis reveals a nuanced pattern: while moderate AI implementation achieves the best TFP, excessive and insufficient implementation yields diminishing returns. The curvature of this inverted U-shaped relationship flattens with higher levels of digital infrastructure quality but steepens when firms undertake diversified businesses and face heightened demand uncertainty. The findings suggest that the impact of AI on TFP is not universally beneficial, and the relationship between AI and TFP varies across different contexts. These findings also provide implications on how firms can strategically implement AI to maximize its value.

In the wave of digital and intelligent transformation, corporate governance is undergoing profound changes. This paper, from the perspective of green governance and sustainable development, explores the new trends in corporate governance under this background, such as data-driven decision-making and the application of intelligent technologies in supervision; analyzes the new challenges faced, including data security and privacy protection, and the digital divide; and based on relevant theories, combined with practical cases and using data models and other methods, explores new paths, aiming to provide theoretical and practical guidance for enterprises to achieve the coordinated and simultaneous progress of digitalization, intelligentization, greenization, and sustainable development.

We investigate the economic impact of artificial intelligence (AI) adoption in the mutual fund industry by introducing a novel measure of AI adoption based on the presence of AI skilled personnel at fund management firms. We provide robust evidence that AI adoption enhances fund performance, primarily by improving risk management, increasing attentive capacity, and enabling faster information processing. Furthermore, we find that mutual funds with higher levels of AI adoption experience greater investor net flows and exhibit lower flow-performance sensitivity. While AI adoption benefits individual funds, we find no evidence of aggregate performance improvements at the industry level.

This paper examines how the integration of artificial intelligence (AI) and big data affects banking operations, emphasizing the crucial role of big data in unlocking the full potential of AI. Leveraging a comprehensive dataset of over 4.5 million loans issued by a leading commercial bank in China and exploiting a policy mandate as an exogenous shock, we document significant improvements in credit rating accuracy and loan performance, particularly for SMEs. Specifically, the adoption of AI and big data reduces the rate of unclassified credit ratings by 40.1% and decreases loan default rates by 29.6%. Analyzing the bank's phased implementation, we find that integrating big data analytics substantially enhances the effectiveness of AI models. We further identify significant heterogeneity: improvements are especially pronounced for unsecured and short-term loans, borrowers with incomplete financial records, first-time borrowers, long-distance borrowers, and firms located in economically underdeveloped or linguistically diverse regions. Our findings underscore the powerful synergy between big data and AI, demonstrating their joint capability to alleviate information frictions and enhance credit allocation efficiency.

Firstly, the article elaborates on the concepts of metaverse and industrial metaverse, pointing out that the metaverse has driven changes and optimizations in multiple dimensions such as urban form, social organization form, and industrial production form; Secondly, the metaverse has empowered urban governance in Guangzhou, improving the efficiency of urban management, enhancing the city's emergency management capabilities, improving the quality of interaction between people and the city, and promoting the construction of a smart city; Once again, the focus was on the practices and good results achieved by Guangzhou in utilizing blockchain technology, digital twin technology, generative artificial intelligence technology, unmanned aerial vehicles+AI and other technologies in urban governance and serving the public; Finally, it is clarified that metaverse related technologies will promote the integration of carbon based civilization and silicon-based civilization in urban and social governance. Humans can use silicon-based civilization technology to expand their living space and improve their quality of life, while silicon-based civilization can also draw inspiration from the culture and emotions of carbon based life, achieving more comprehensive development.

Based on the panel data of listed manufacturing companies in China from 2010 to 2019, the artificial intelligence (AI) index is constructed using the industrial robot data provided by the International Federation of Robotics, and the two-way fixed effect model is used to test the impact of AI on the total factor productivity (TFP) of enterprises. The results show that AI significantly improves the TFP of manufacturing enterprises, and this conclusion remains valid after robustness tests and endogeneity processing. AI promotes TFP by improving the level of human capital and technological innovation, and management and operational levels positively regulate the promotional effect of AI on the TFP of enterprises. Compared with manufacturing enterprises in the central and western regions, AI boosts the TFP of those in the eastern region; compared with non-state-owned enterprises, AI boosts the TFP of state-owned enterprises; and AI significantly boosts the TFP of high-tech and non-high-tech enterprises.

Our study explored the rise of public companies competing to launch large language models (LLMs) in the Chinese stock market after ChatGPTs' success. We analyzed 25 companies listed on the Chinese Stock Exchange and discovered that the cumulative abnormal return (CAR) was high up to 3% before LLMs' release, indicating a positive view from insiders. However, CAR dropped to around 1.5% after their release. Early LLM releases had better market reactions, especially those focused on customer service, design, and education. Conversely, LLMs dedicated to IT and civil service received negative feedback.

In today’s global business landscape, successful cross-cultural collaboration is not just an asset but a necessity. This article explores essential strategies for achieving effective collaboration between Chinese and Canadian business teams, emphasizing the critical role of cultural intelligence. We delve into understanding cultural differences, their impact on decision-making and communication, and the keys to building high-performing cross-cultural teams. Real-world case studies highlight success stories, and actionable strategies for bridging cultural gaps are provided. By embracing these insights, businesses can unlock the potential of cross-cultural cooperation, leading to better decision-making, enhanced communication, and stronger, more effective teams.