Extending Network Tools to Explore Trends in Temporal Granular Trade Networks

Bilateral trade relationships have been under increased scrutiny by the economic literature for their relevant role in influencing economic growth and, more in general, for their impact in the geopolitical sphere. Many studies try to understand the determinants and dynamics of trade flows. However, only a few have leveraged the growing availability of granular data to consistently analyze specific sectors of the economy and address the challenges posed by contemporary issues, such as climate change, for which understanding the temporal trade dynamics can be critical in the rush toward the green transition. This work aims to fill this gap by implementing a module to model and directly preprocess the most updated granular trade data in a network and by proposing a simple yet effective methodology to identify the evolving dynamics of temporal networks. As a use case, we focus on trades related to semiconductors and minerals relevant to energy storage, namely lithium and graphite, both relevant sectors for the energy transition, and, employing our novel approach, we uncover recent trends in these markets.