Evaluating the Structural Robustness of Large-Scale Emerging Industry with Blurring Boundaries

The present large-scale emerging industry evolves into a form of an open system with blurring boundaries. However, when complex structures with numerous nodes and connections encounter an open system with blurring boundaries, it becomes much more challenging to effectively depict the structure of an emerging industry, which is the precondition for robustness evaluation. Therefore, this study proposes a novel framework based on a data-driven percolation process and complex network theory to depict the network skeleton and thus evaluate the structural robustness of large-scale emerging industries. The empirical data we used are actual firm-level transaction data in the Chinese new energy vehicle industry in 2019, 2020, and 2021. We applied our method to explore the transformation of structural robustness in the Chinese new energy vehicle industry in pre-COVID (2019), under-COVID (2020), and post-COVID (2021) eras. We unveil that the Chinese new energy vehicle industry became more robust against random attacks in the post-COVID era than in pre-COVID.

Automated summary

This study proposes a novel framework based on a data-driven percolation process and complex network theory to depict the network skeleton and evaluate the structural robustness of large-scale emerging industries. By applying this method to actual firm-level transaction data in the Chinese new energy vehicle industry, we uncover the changes in structural robustness before and after the COVID-19 pandemic.