Coordinated development between new-type urbanization and rural revitalization is important for sustainable urban–rural transformation and balanced regional development in China. Using panel data for 30 provincial-level units from 2014 to 2023, this study examines the spatiotemporal evolution, dynamic transitions, and external drivers of the coupling coordination degree between the two systems. Spatial Markov chains and an interpretable machine-learning framework are used to identify neighborhood effects, nonlinear relationships, and interaction patterns. The results show four main findings. First, the coupling coordination degree increased over the study period, but clear spatial differences and clustering remained. This suggests that coordinated urban–rural development did not advance evenly across regions. Second, the evolution of coordination shows strong state dependence, and neighborhood context is closely related to transition probabilities. Provinces located in high-coordination neighborhoods were more likely to move to higher levels, while provinces in low-coordination neighborhoods were more likely to remain trapped at lower levels. Third, digital inclusive finance and fiscal self-sufficiency were the most important external factors. Both showed clear nonlinear patterns. Per capita electricity consumption and aging rate also showed heterogeneous relationships at different value ranges. Fourth, the interaction results suggest that higher coordination is more likely to emerge when digital finance, fiscal capacity, openness, human capital, and infrastructure improve together, rather than when only one factor expands on its own. The findings indicate that sustainable urban–rural transformation is shaped by spatial dependence, nonlinear changes, and context-specific factor combinations. Beyond their relevance for more targeted urban–rural coordination and place-based sustainability governance in China, these findings also provide a useful reference for other developing countries seeking to address similar urban–rural development challenges.
Wang et al. (Tue,) studied this question.