|Timeline:||April 2020 - 15 April 2024|
|Sources of funding:||Own accounter|
In order to achieve the established climate goals, clean energy industries are of high importance for policymakers with an additional promise of creating numerous new jobs. In the context of grand societal challenges, such as climate change and the associated transition toward renewable energies, the understanding of regional diversification (related or unrelated), and knowledge and its characteristics becomes particularly important for strategies aiming to develop new green paths or strengthening established green regimes. While the recent policy-related conceptual advancements ranging from related variety, local path dependencies, creation of regional advantages, and smart specialisation are centred around knowledge exchange and learning processes, the operationalisation of knowledge-related conceptual elements still remains a demanding task. Moreover, studies in economic geography usually focus on the role of the (local) supply side in the emergence and evolution of industries, emphasising agglomeration externalities, windows of local opportunity, path dependence, and related variety, and have been less focused on developing hypotheses around multi-scalar drivers, vested interests, competing technologies, unrelated variety and the simultaneous analysis of demand and supply.
My research aims to investigate the key factors responsible for the emergence and evolution of the various clean-energy sectors, specifically the wind, solar energy and electric vehicle sector, as well as the key factors driving innovation within them. It aims to use network measures to propose a novel operationalisation of knowledge at the aggregate level and operationalises non-local knowledge sourcing through an antecedent-descendent dataset, thereby investigating the relevance of multi-scalar (local and non-local) knowledge dynamics in both the evolution of the various clean-energy sector as well as in innovation dynamics. An improved understanding of knowledge and its characteristics – including its diffusion, creation, the qualitative definition of aggregate knowledge and evolution of knowledge networks within the various clean energy sectors is expected to positively influence innovation policies by helping policymakers shift their focus from correcting for market failures to more systematic problems.
The research also intends to study the co-evolutionary dynamics between the local knowledge production and demand creation, as well as the competing technologies contesting the process of diversification. Finally, the research aims to investigate the resilience of regional knowledge production, and niche and regime formation dynamics, when exposed to external (economic) shocks and tries to simulate possible regional resilience scenarios. The study makes use of statistical analysis methods (including, OLS regression, Bayesian survival analysis, etc.), network analysis methods (SNA, SAOM, etc.) as well as an agent-based simulation model for the above-intended purposes.