Abstract
This paper brings together the key concepts of policy learning and central-local relations to examine how the efficacy of sustainability policies can be improved, with a particular reference to pricing policies for wind energy in China. Based on our comparative case studies of three provinces, Guangdong, Shanghai and Xinjiang, we critically examine how central-local relations may facilitate or impede policy learning. Our analysis focuses on policy changes at the national level, including the move away from the tendering policy to a fixed-price policy in 2009, and the diversity of local policy responses, which include a local fixed-price policy in Guangdong, a two-tiered model in Shanghai and a de facto fixed-price policy in Xinjiang. We have three major key findings. First, we found that technical and conceptual forms of policy learning have taken place in China, but the progression towards the highest form of policy learning, social learning, is limited. Secondly, we found that the established fabric of central-local relations has created facilitating conditions for as well as limitations to the advancements towards social learning. A national policy framework, a multi-level governance system, institutional arrangements for knowledge creation and learning, and a more participatory form of governance for civil society are some of the facilitating conditions. However, over-centralization, the inertia against institutional changes and the failure to recognize the need for a more deliberative decision-making process are identified as key barriers.
| Original language | English |
|---|---|
| Pages (from-to) | 216-232 |
| Number of pages | 17 |
| Journal | Environmental Policy and Governance |
| Volume | 24 |
| Issue number | 3 |
| Early online date | 14 Mar 2014 |
| DOIs | |
| Publication status | Published - May 2014 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
User-Defined Keywords
- Central-local relations
- China
- Energy pricing
- Policy learning
- Wind policy
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