China’s Energy Risk Spillover Networks Under Major Events and External Uncertainty Shocks: An Analysis Based on LASSO-VAR-DY and TVP-SV-VAR Models

Major events and external uncertainty shocks have made energy risk connectedness increasingly complex. This paper applies a LASSO-regularized VAR combined with the Diebold-Yilmaz connectedness framework (LASSO-VAR-DY) to trace how China’s energy risk spillover effects evolve under major event shocks and to quantify sectoral risk spillover inflows. We then employ a TVP-SV-VAR model to further examine the impulse responses of energy sectors to external uncertainties. The results show that the energy system exhibits a high overall level of risk connectedness with pronounced stage-wise variation and is sensitive to different external uncertainty shocks. Major-event shocks intensify sector-level risk connectedness—the clean-energy sector consistently acts as a net risk receiver. In contrast, other sectors switch between net transmitters and net receivers across shocks. Different major events operate through heterogeneous mechanisms—the COVID-19 pandemic and the official launch of the national carbon market primarily strengthen node-to-node connectedness. In contrast, the Russia-Ukraine conflict chiefly amplifies spillover intensity between nodes. The effects of uncertainty index shocks differ markedly: economic policy uncertainty (EPU) has the most substantial impact, followed by climate policy uncertainty (CPU), while geopolitical risk (GPR) is the weakest.