Unraveling the dynamics and drivers of the global wind turbine trade network

The global wind turbine trade network (GWTTN) is a crucial component of the renewable energy transition, facilitating the diffusion of wind power technologies across borders. To analyze the structural complexities of this system, this study models the GWTNN where nodes represent countries, and directed links denote bilateral wind-turbine trade relationships (weighted by trade value). However, the structural dynamics and determinants of this network remain insufficiently understood, hindering evidence-based policymaking. To bridge this gap, this study provides a comprehensive analysis of the evolution of the GWTTN from 2002 to 2022. Temporal exponential random graph models are employed to examine the influence of endogenous structural effects, country-specific attributes, and relational factors on network formation. Strong transitivity, preferential attachment, and temporal stability effects are observed, indicating the presence of persistent trade hubs and a tendency for clustering. Notably, trade predominantly occurs between countries with dissimilar income levels but similar climate commitments, highlighting the network's dual role in enabling technology transfer and supporting climate policy implementation. Furthermore, geographical proximity, cultural ties, and free trade agreements emerge as significant drivers of trade patterns. These findings offer actionable insights for policymakers and industry stakeholders, emphasizing the need for targeted interventions to support emerging market participants, promote balanced technology transfer, and leverage international climate agreements. By elucidating the complex dynamics of the GWTTN, this study contributes to a more nuanced understanding of global renewable energy diffusion and provides a foundation for evidence-based decision-making in the ongoing energy transition.