Against the backdrop of accelerating global carbon neutrality and increasingly complex urban decarbonization systems, existing urban energy models still have limitations in system-boundary representation and flexible carbon quota adjustment. A decarbonization pathway optimization method for metropolitan areas based on dynamic carbon quota allocation is proposed. First, multisource data are integrated, and the LEAP model is employed to simulate the evolution of urban energy demand and carbon emissions under different policy scenarios. Second, a government-industry Stackelberg game is formulated, in which the urban carbon emission constraint is embedded into the industry quota adjustment process, and the quota adjustment coefficient and optimal output are derived analytically. Third, emission-reduction cost perturbation scenarios are established to analyze changes in sectoral emission-reduction contributions, timing, and technology substitution intensity. Finally, a case study of Tianjin is conducted. The results show that the decarbonization pathways of key urban sectors can be identified by the proposed method. Compared with static allocation, the dynamic quota mechanism achieves greater emission reductions while balancing enterprise profit and government social welfare.