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Exploring coherence, learning and directionality in policy mixes for sustainability transition: the case of the Norwegian maritime transport’s decarbonization

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Abstract

Achieving Sustainability Transition (ST) targets poses challenges for public authorities, decision-makers, and policies. It is not enough to rely on a single policy instrument. Policy researchers have yet to fully explore the reasons and methods for policy mixes and how different policies interact. This article looks at two critical questions regarding policy mixes: Do they align with each other both vertically and horizontally? Furthermore, do they continue to move in a consistent direction as they learn? ST policies are generally conceived under a comprehensive vision and a societal change perspective. However, studies still need to attempt to combine the concepts of learning and directionality into a time perspective for evaluating the ST policy mix. In this vein, the paper aims to design a conceptual framework for bridging this gap and consider policy mix evaluation as a continuous nested process rather than a sequential process. To answer the two previous questions, we applied this conceptual framework to a case study to evaluate the coherence of a specific ST policy mix via learning and directionality. Our findings demonstrate that policy coherence can be understood not only through static interactions but also through dynamic interactions between objectives and instruments.

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Data availability

The author confirms that all data generated or analysed during this study are included in this published article. Furthermore, primary and secondary sources and data supporting the findings of this study were all publicly available at the time of submission.

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  1. University of Quebec at Rimouski, 300, Allée Des Ursulines, C.P. 3300, Succ. A, Rimouski, QC, G5L 3A1, Canada

    Sofiane Laribi

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Laribi, S. Exploring coherence, learning and directionality in policy mixes for sustainability transition: the case of the Norwegian maritime transport’s decarbonization. GPPG (2024). https://doi.org/10.1007/s43508-024-00093-6

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