Dynamic Incentive Design for Green Bonds: Signaling, Reputation, and Optimal Subsidies

This paper develops a dynamic Stackelberg model to analyze how a regulator should design incentives in green bond markets when information is asymmetric, firms face heterogeneous costs, and reputational payoffs evolve over time. Firms differ in their environmental type and choose to issue green bonds if their private cost falls below an endogenous threshold shaped by economic returns, policy incentives, and public beliefs about their quality. These beliefs are updated through Bayesian inference from observed issuance behavior, creating a link between current actions and future reputational rewards. The regulator seeks to maximize dynamic social welfare by trading off the immediate benefits of greater issuance against the fiscal cost of subsidies and the longer-term compliance gains generated by reputation. Under tractable functional assumptions, we derive closed-form expressions for the optimal subsidy and compare static (myopic) and dynamic policy rules. The analysis shows that when reputational spillovers are strong, dynamic optimization leads to lower subsidies because belief updating amplifies future participation. Conversely, when beliefs adjust slowly or firm types are poorly distinguishable, higher subsidies are needed to sustain credible signaling.The results highlight the importance of reputation as a cost-effective complement to direct subsidies and offer guidance for scaling green finance while containing public expenditure.