Functional differentiation pyramid and adaptive reconstruction: a computational analysis of multi-level governance in China’s policy experimentation zones

Reconciling strategic alignment with local innovation is a core governance challenge for policy experimentation zones worldwide. This study investigates this challenge by elucidating the operational dynamics of multi-level governance in China, using the unique Guangdong-Macao In-depth Cooperation Zone in Hengqin as a natural laboratory for institutional experimentation. Integrating semantic network analysis (SNA) and Latent Dirichlet Allocation (LDA) topic modeling, we systematically analyze 623 policy documents from 2009 to 2024, spanning four administrative levels: national, provincial, municipal, and cooperation zone. Our findings reveal a “functional differentiation pyramid” governance architecture, where the national level provides strategic guidance, while lower-level governments engage in significant issue disaggregation and reorganization during implementation. We find that policy transmission across levels is not a linear replication but a dynamic process we term “adaptive reconstruction”—a form of institutional work actively undertaken by subnational governments to enhance policy fitness. We argue that this mechanism is key to how the governance system simultaneously maintains strategic coherence and local flexibility. The results provide new empirical evidence for multi-level governance (MLG) theory. This “Hengqin model” contributes to global governance debates by empirically demonstrating a distinct architecture for policy implementation, while also showcasing the potential of computational social science in policy analysis.