How regulatory intensity shapes policy diffusion: Incentive-penalty synergies in Forest Eco-Bank governance

A central challenge facing China's “Forest Eco-Bank” (FEB) lies in achieving widespread and sustained participation among smallholders within a context of fragmented property rights. To address this, this study constructs a micro-decision diffusion framework that integrates Prospect Theory with complex network evolutionary game theory. We define “regulatory intensity” as a composite variable reflecting both government enforcement vigor and policy credibility. The results indicate that: (1) Under medium-to-high regulatory intensity, a strategy of “parallel incentives and punishments” emerges as the optimal steady-state equilibrium. (2) However, from a dynamic evolutionary perspective, significant differences exist in instrument switching. Under dual administrative and fiscal resource constraints, a “penalty-first, incentive-later” strategy proves superior to an “incentive-first, penalty-later” approach for establishing baseline rules and locking in high-level steady states. (3) Regulatory intensity effectively corrects behavioral deviations by enhancing institutional credibility—specifically, by reinforcing the certainty of policy fulfillment to revise subjective probability perceptions, thereby mitigating loss aversion. (4) Furthermore, social network structures shape differentiated diffusion paths: hierarchical networks leverage the hub role of core nodes to accelerate diffusion, whereas edge nodes in flat networks risk falling into “structural isolation” due to a lack of effective connectivity. Policy implications suggest a shift toward “adaptive governance”: flexibly adjusting the timing of instruments according to diffusion stages, strengthening the certainty of policy fulfillment to lower expected loss risks, and utilizing network structures for targeted interventions.