Evolutionary theorizing and modeling of sustainability transitions

This paper argues that evolutionary thinking and modeling can contribute to the emerging research on sustainability transitions and their management. Evolutionary theory provides a range of concepts and mechanisms that are useful in making existing theorizing about transitions more precise and complete. In particular, we will discuss how the multi-level, multi-phase, co-evolutionary, and social learning dynamics underlying transitions can be addressed in evolutionary models. In addition, evolutionary theorizing offers suggestions for extending current theoretical frameworks of transitions. Group selection provides a good example. We review the small set of formal evolutionary models of sustainability transitions, and show that existing formal evolutionary models of technological, social and institutional change can provide useful inputs to transition research and management.     

The verticality of policy mixes for sustainability transitions: A case study of solar water heating in China

Climate change and sustainable development are the defining challenges of the modern era. The field of sustainability transitions seeks systematic solutions for fundamental transformations of socio-technical systems towards sustainability, and exploring the role of policy mixes has been a central research agenda within the field. In the extant literature, there exists a lack of both conceptual and empirical research on the vertical dimension of policy mixes for sustainability transitions. This study provides a multilevel and evolutionary interpretation of the vertical interactions of policy mixes in the process of industrial path development towards sustainability transitions. An exemplary case of solar water heating (SWH) technology in Shandong Province in China is presented, capturing both the bottom-up and top-down processes of interactions. On the one hand, urban-level policy initiatives can inform higher-level policymaking; on the other hand, national-level priorities can greatly configure policy strategies for lower-level governments. Moreover, as the industry matures, the interactions of multilevel policy mixes evolve from simpler, unidirectional patterns to more complex, bidirectional ones through a vertical policy-learning feedback mechanism. This study generates two important policy implications that extend beyond the Chinese context: first, dynamic vertical interactions between multilevel governments indicate the need to develop more nuanced perspectives on the design of policy mixes; second, policy makers need to hold a more dynamic view of policy mixes by recognizing their temporal and coevolutionary nature through the policy learning process.