A Multi-State Epidemic Model with Cellular Automata for Liquidity Risk Contagion in High-Carbon Manufacturing
Year: 2026
Type: article
Abstract: From the executive risk preference–policy uncertainty interaction perspective, this paper constructs a CA-SHIRD model by leveraging complex network theory and cellular automata, and further analyzes the liquidity risk contagion mechanism and dynamic evolutionary characteristics in the high-carbon manufacturing industry. In summary, this paper yields several key findings: (1) An increase in infection probability intensifies liquidity risk contagion, while a higher transition probability mainly accelerates short-term risk exposure. A higher recovery probability mitigates the contagion process and increases the proportion of recovered enterprises, whereas a higher default probability also weakens contagion but leads to a larger proportion of defaulted enterprises. (2) The liquidity risk contagion threshold in the high-carbon manufacturing industry is positively correlated with policy sensitivity, policy target ambiguity, and policy change frequency; negatively correlated with green preference, long-term orientation, and policy coordination; and exhibits a U-shaped relationship with risk perception and an inverted U-shaped relationship with policy instrument predictability. (3) When policy sensitivity interacts with policy change frequency, it exerts a risk resonance effect; when policy target ambiguity interacts with green preference and long-term orientation, a risk hedging effect is generated; when policy change frequency interacts with green preference and long-term orientation, the risk amplification effect of policy change frequency dominates the risk mitigation effect of green preference and long-term orientation.
Source: Mathematics
DOI: 10.3390/math14173206
Authors: Jining Wang, Gengchen Zhao, Lei Wang, Han Sun
Institution: Nanjing Tech University
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7208739281