Policy mixes and the policy learning process of energy transitions: Insights from the feed-in tariff policy and urban community solar in Hong Kong

Daphne Ngar-yin Mah, Darren Man-wai Cheung, Michael K.H. Leung, Maggie Yachao Wang, Mandy Wai-ming Wong, Kevin Lo and Altair T.F. Cheung

Energy Policy, 2021, vol. 157, issue C

Abstract: Effectiveness of renewable energy feed-in tariff (FiT) varies across the world. Designing policy mixes of a package of policy instruments to optimise the normative effect of FiT is critical but has remained challenging and under-studied. This paper brings together the key concepts of policy mixes and policy learning to examine how the effectiveness of renewable energy policies can be improved, with reference to a recent FiT policy in Hong Kong focusing two prospective solar communities. Based on 99 in-depth interviews and workshop discussions involving 57 householders, we found that FiT was an effective policy in stimulating growth of new solar photovoltaic (PV) projects in some sub-sectors in Hong Kong, but has not yet mainstreamed solar at the community and city levels. The FiT was insufficient to address multiple non-economic barriers perceived by householders. The limited policy impacts of the FiT indicated that policy makers were able to attain technical learning, but faced major constraints in advancing to conceptual and social forms of policy learning. This paper concludes that policy makers should give closer attention to policy mixes and advanced forms of policy learning than choosing a single “most effective” policy instrument to unlock the under-used community solar potentials.

Keywords: Renewable energy feed-in tariff; Policy mixes; Policy learning; Community solar; Hong Kong (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:157:y:2021:i:c:s0301421521000835

DOI: 10.1016/j.enpol.2021.112214

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