Analysis of Key Risk Factors in the Thermal Coal Supply Chain

The thermal coal supply chain serves as core infrastructure for ensuring the safe and stable supply of electricity in China. Effective risk management and control of this supply chain are therefore critical to national energy security and socio-economic development. However, the thermal coal supply chain involves multiple complex risk dimensions, including cross-regional multi-entity coordination, a complex network structure, and a dynamic policy environment. Traditional risk analysis methods often fall short in depicting the concurrent events and dynamic propagation characteristics inherent to such a system. This necessitates systematically investigating the thermal coal supply chain within the Coal–Electricity Joint Venture (CEJV) operational framework, which primarily involves equity-based consolidation and long-term contractual coordination between coal producers and power generators, to comprehensively analyze its critical risk factors and transmission mechanisms. Initially, based on the integration of coal-fired power joint operation policy evolution and industry characteristics, 28 risk factors were identified across three dimensions: internal enterprise, external environment, and overall structure. These encompassed production fluctuation risks, thermal coal transport process risks, and insufficient supply chain flexibility. A dynamic behavior model for the thermal coal supply chain was constructed by analyzing the causal relationships among these risk factors, based on the operational processes of each link. Utilizing Petri net simulation technology enables a quantitative analysis of supply chain risks, facilitating the identification of bottleneck links and potential risk points. Through model simulation, 18 key risk factors were determined, providing a theoretical basis for optimizing supply chain resilience within CEJV enterprises. The limitations of traditional methods in dynamic process modeling and industrial applicability were addressed through a Petri net-based methodology, thereby establishing a novel analytical paradigm for risk management in complex energy supply chains.