Policy Credibility and Carbon Border Adjustments: A Dynamic Signaling Analysis

This study examines how information frictions in climate policy credibility shape carbon border adjustment mechanisms when trading partners cannot fully verify each other’s commitment to green industrial policies. A dynamic signaling framework models exporters’ policy commitment capacity as private information, incorporating Bayesian belief updating subject to signal noise and reputation decay. The analysis derives a Perfect Bayesian Equilibrium characterizing optimal CBAM tariff responses conditional on importers’ evolving credibility assessments. The calibrated model achieves strong empirical validation (R2 = 0.884, explaining 88% of tariff variance; rank correlation ρ=0.950), with Monte Carlo simulations demonstrating robust internal consistency (RMSE = 2.56 percentage points). Results identify a critical belief threshold (pt<0.3) triggering hyperelastic tariff responses: below this credibility level, small perception declines generate disproportionately steep border tax increases (elasticity ≥ 2), trapping countries in high-tariff equilibria despite genuine commitment. Information frictions impose aggregate welfare losses equivalent to 30% of potential coordination gains, decomposed into four sources: information opacity (40%), cognitive biases in belief formation (27%), policy distortions induced by credibility concerns (20%), and reputation maintenance costs (13%). These quantitative patterns, while illustrative within the baseline calibration, motivate testable implications regarding elasticity asymmetries and credibility persistence. The framework identifies targeted policy interventions—third-party verification of commitment durability, rule-based tariff adjustment protocols, and institutional commitment devices—systematically prioritized by marginal welfare impact to guide beliefs away from credibility traps while maintaining environmental rigor.