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A global-scale study on decision making in renewable energy policy: Internal and external factors driving the adoption of Feed-in Tariffs and Renewable Portfolio Standards

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To accelerate the clean energy transition, it is necessary to better understand the global policy dynamics and motivations behind clean energy policy adoptions and diffusion. This paper examines the differential roles of internal and external diffusion factors on decisions to adopt renewable energy policies, i.e., feed-in tariffs (FIT) and renewable portfolio standards (RPS), employing a unique blend of cluster analysis and event history analysis. Cluster analysis uncovers a dichotomy in adopting countries. Firstly, early adopters emerge as high-income, democratic countries, mostly energy importers with high CO2 emissions, with OECD and EU membership. Conversely, the second cluster consists of adopters from middle-income, non-OECD, and non-EU nations with lower CO2 emissions. Strikingly, these clusters align with UNFCCC party classifications, underscoring the pivotal role of international agreements. Event history analysis suggests that especially environmental pressure, but also democratic governance and economic development are important when adopting renewable energy policies. Interestingly, environmental endowments seem to be less important. These findings lead to an important question: Are the requisite policy tools readily available to navigate diverse socio-economic, political, and environmental landscapes, and are they deployed in a timely fashion? Beyond these findings, the study also shows that policy diffusion especially through normative emulation and social learning – operationalized as political globalization, OECD member and following regional neighbors – are important for policy adoption, suggesting the importance of such tools for promoting policies. Moreover, distinctive factors come into play when examining the adoption of FIT versus RPS policies, emphasizing the need for nuanced policy approaches.



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Environmental Policy and Governance

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ERP Environment
This work was supported by the Hong Kong Research Grants Council [General Research Fund Project 16602318].
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