AI-assisted bilevel optimization for sustainable maritime operations under the EU emissions trading system: model-implied carbon-cost signals and shipping network response

Maritime decarbonization is increasingly urgent as the European Union Emissions Trading System (EU ETS) extends carbon pricing to liner shipping. This study develops an AI-assisted bilevel framework combining Bayesian Optimization (BO) and Mixed-Integer Linear Programming (MILP) to link an emission-indexed support path, a unit-consistent Marginal Abatement Cost–Total Number of Allowances in Circulation–Market Stability Reserve (MAC–TNAC–MSR) feedback mechanism, and discrete carrier network responses. In the Asia–Europe case, a representative best-evaluated policy reduces cumulative physical and ETS-covered emissions by 28.39% and 32.94%, respectively, relative to a matched nine-hub baseline, while increasing resource cost by only 0.213%. It captures 85.91% of the same-constraint short-run abatement potential. In an exact 248-policy micro-instance, BO achieves a 0.00% median optimality gap, a 4.35% mean gap, and an 80.0% exact-hit rate across 30 seeds under a common 20-evaluation budget. These results demonstrate the sample efficiency of BO–MILP for discontinuous maritime policy-response problems and provide practical guidance for designing support policies that promote lower-emission vessel deployment without substantial additional resource costs.