Adaptive water governance plays an increasingly important role in sustainable urban development and water governance response to global climate change. To comprehensively understand the research situation and development trend of adaptive water management, this study conducts a systematic literature review of articles published in International Social Sciences Citation Index (SSCI) journals based on bibliometric analysis. The results show that adaptive water governance as an emerging topic of water governance has an overall growth trend in published articles. The articles on adaptive water governance mainly concentrated on the disciplines of environmental science and environmental studies. Developed countries are a hub for water governance research, and China has the sixth largest number of articles from around the world. The adaptive water governance research has formed a preliminary global collaborative network, but the authors' collaboration needs to be strengthened. The most popular topics of adaptive water governance include South Africa, adaptive management, groundwater, principal component analysis, scenario planning, the analytic hierarchy process, resources, basins, computer experiments, and technology development. This finding suggests that adaptive water governance is a critical driver for sustainable urban development and represents a critical direction in the future research of water governance.

How public policies change incrementally over time remains understudied. This paper contributes to the studies of incremental policy change by integrating the theories of policy layering and learning into a theoretical framework of experimental governance (EG). Using a mixed research method, we apply this framework to process-tracing the changing trajectory of China’s central government infrastructure public-private partnership (PPP) policies from 1988 to 2017 by looking at evolving policy goals, policy measures, and policy co-issuing networks. Results suggest that China’s central government infrastructure PPP policy change follows a refined EG approach in which policies change incrementally in a layering pattern, primarily driven by learning. Findings provide a new account of incremental policy change.