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.

Despite the lack of electoral accountability, China has built an expanding welfare system that is set to include most citizens. Why does China defy the conventional prediction of an exclusive autocratic welfare state? This paper looks at the critical time when China first established its social security system in the 1990s and argues that the state adopts a “demand-driven strategy” where the redistribution effort varies with the expected collective action of economic losers. Analyzing an original granular county-level dataset of China’s laid-off workers and social security taxation, the paper finds that a group of newly-emerged economic losers, precipitated by state policy, drives the local states’ efforts to redistribute. In particular, the number of laid-off state-owned enterprise workers explains 46% of the variations in social security collection among non-state-owned enterprises. Instrumental variable estimation, with legacy state-owned enterprises established in historical contingencies as the instrument for laid-off workers, shows consistent results. Further analysis on mechanisms demonstrates that layoffs lead to an increase in SOE protests, which in turn foster greater redistribution.