Unlocking ecosystemic green intellectual capital: a multi-agent simulation of institutional thresholds and dynamic knowledge flows

Purpose This study investigates how Green Intellectual Capital (GIC) coordinates heterogeneous agents across innovation ecosystems through dynamic knowledge flows. While prior literature heavily focuses on static, firm-level GIC “stocks,” the dynamic processes through which green human capital, green structural capital (such as green patents), and green relational capital (such as market legitimacy) flow across organizational boundaries remain theoretically underdeveloped. This paper addresses this gap by examining how external institutional and market pressures act as catalytic control parameters to trigger systemic phase transitions in ecosystemic GIC flows. Design/methodology/approach Drawing on Complex Adaptive Systems theory, Control Theory, and the Intellectual Capital-based View, this study constructs a dynamic multi-agent simulation model. The computational framework features heterogeneous agents (firms, research institutions, regulatory entities, and market actors) whose interactions are governed by non-linear GIC flows, knowledge spillovers, and boundary-spanning legitimacy transmission. The model’s parameters and critical threshold mechanics are evaluated under varying degrees of regulatory intensity and market legitimacy pressures. Findings The simulation reveals that green innovation ecosystems exhibit non-linear, threshold-driven phase transitions. Below critical institutional (μ* = 1.89, λ1 = 1.89, p < 0.001) and market legitimacy µ* = 1.45, λ2 = 1.45, p < 0.001) thresholds, GIC flows remain fragmented, leading to low-activation equilibria characterized by symbolic compliance. Once both control parameters simultaneously cross their critical tipping points, GIC flows self-organize into highly coordinated patterns, achieving a 165% increase in aggregate ecosystem environmental performance and unlocking a 509.5% synergistic amplification in system-level green patent innovation coefficients (shifting from 0.567 in the sub-critical regime to 3.456 in the super-critical regime). Originality/value This study offers three core contributions to the intellectual capital literature: (1) it advances GIC from a static, firm-level asset perspective to an ecosystemic dynamic flow model governed by control theory; (2) it delineates the non-linear “conversion loop” wherein structural and relational capitals exert crucial dynamic feedback on human capital accumulation; and (3) it introduces multi-agent computational modeling and control parameter testing to the IC field, providing a rigorous virtual laboratory for sustainable policy design.