Combining Rules and Capacity: Domestic Sources of China’s Dual Approach in Global AI Governance
Year: 2026
Type: article
Abstract: China has actively expanded its international influence in both technological innovation and global technology governance under the Xi Jinping administration. Over the past decade, its engagement in global AI governance has evolved into a dual pathway characterised by rule-based and capacity-based approaches. Existing scholarship has provided valuable insights into China’s strategic positioning in global AI governance and the roles of domestic actors in shaping AI policy. However, these literatures remain insufficiently integrated, with limited systematic analysis of how domestic policy debates among actors are translated into China’s dual approach towards global AI governance. This article addresses this gap by examining how domestic AI policy debates shape China’s dual pathway in global AI governance. It employs the Advocacy Coalition Framework and Discourse Network Analysis to analyse how interactions among diverse domestic actors influence the central government’s expressed preferences. The findings demonstrate that China’s dual approach emerges from domestic policy debates in which shared techno-globalist beliefs are interpreted and operationalised differently across actor groups. While techno-globalism functions as a common discursive framework, policy differentiation occurs at the level of policy solutions. In this process, the central government acts as a strategic integrator, selectively combining heterogeneous policy signals into a coherent governance strategy that links rule-shaping with capacity-building. By connecting domestic policy processes with global governance outcomes, this study contributes to a more integrated understanding of China’s role in the governance of emerging technologies. It also advances the application of policy process theory by demonstrating how the ACF and DNA can be adapted to analyse policymaking dynamics in the Chinese context.
Source: Geopolitics
Authors: Hengyi Yang, Catherine Yuk-Ping Lo
Institution: Maastricht University
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Award: G5112087831
OpenAlex ID: W7168554478