Navigating systemic risk: a dynamic analysis of multilayer interdependencies in the energy industrial chain

Purpose This study aims to investigate the risk spillover dynamics among 21 industries within the Chinese energy industrial chain, emphasizing the intricate interdependencies shaped by geopolitical factors and the evolving complexity of the global energy ecosystem. Design/methodology/approach This paper uses the risk spillover index constructed by 2012, hereafter Diebold–Yilmaz (DY) as a measure of risk transmission between industries and firms. This paper also uses the Vector Autoregression (VAR) model (Koop et al., 1996; Pesaran and Shin, 1998) to quantify the percentage of risk shock errors between two different industries through the H-step ahead generalized forecast error variance decomposition. This paper finally aims to construct and analyze a Quantile Vector Autoregression (QVAR) model for examining the interactions of variables under varying conditions, particularly across different quantiles. Findings The findings indicate that both industries and enterprises within China’s energy sector exhibit significant levels of risk, with a considerable intensity of risk spillover along the industry chain, strongly correlated with external factors and displaying notable characteristics. By integrating the study of geopolitical risks, this study finds substantial differences in the correlation between risk spillover in China’s energy industry chain and geopolitical risks across different quantiles, indicating asymmetry. Originality/value This study pioneers a dynamic, multilayer analysis of systemic risk spillovers within the energy industrial chain, integrating geopolitical factors and quantile-dependent asymmetries. By combining the DY spillover index, VAR-based variance decomposition and QVAR modeling, this study reveals interdependencies across 21 industries, highlighting how external shocks propagate asymmetrically under varying risk conditions. The findings provide insights into the heterogeneous impact of geopolitical risks on different quantiles of the energy chain, offering policymakers and industry stakeholders a refined toolkit for targeted risk mitigation. This work bridges gaps between traditional spillover analysis and geopolitical risk research, advancing methodological and practical understanding of energy market resilience.