Abstract：Policy experimentation is a governance arrangement usually adopted in China's reform， which provides an effective path to steadily promote governance transformation by constructing an enhanced learning mechanism that is driven by issues. Most of the existing studies focus on the organizational model of policy experimentation， whereas little research has been conducted on the policy learning driven by experimentation. From the perspective of issue learning， we construct an analytical framework of “iterative learning and governance transformation”. It is shown that policy experimentation is not a one-shot testing process but is accompanied by continuous iterative learning and knowledge production. Issue learning in policy experimentation can be divided into four stages： the conceptual creation driven by issues， the local experimentation driven by concepts， the policy renewal driven by performance， and the iterative learning driven by issue reconstruction. Successful experiments cause an effect of self-enhancement， and lessons can also be drawn from failed experiments， which will propel policy experimentation towards iterative innovation and the realization of the continuous acquisition and application of knowledge. In the case study， the iterative learning of policy experimentation has been confirmed： after several rounds of policy experimentation， Beijing has smoothly realized the urban grassroots governance transformation.
2023, Vol. 62 
