A multi-model carbon estimation framework for new urban district planning-integrating land use, transportation, and investment-based emission models.

This study proposes an integrated multi-model coupling framework for ex-ante carbon assessment in new urban district planning. The framework combines investment allocation, land-use carbon estimation, and traffic emission models within a unified analytical environment to simulate spatial and economic feedbacks that drive urban carbon emissions. Compared with conventional carbon assessment approaches, which often evaluate land use and transport as independent modules, the proposed framework introduces a rule-based coupling mechanism that dynamically links investment intensity, spatial configuration, and carbon output through iterative feedback. Each sub-model exchanges parameters via a shared data layer, enabling recursive interactions among economic input, land-use change, and mobility patterns. The framework advances urban carbon modeling through three key innovations. First, it establishes an investment-carbon elasticity mechanism that quantifies how capital concentration influences spatial emission patterns. Second, it formulates a rule-based symbolic coupling algorithm that enables cross-model parameter updating during iteration. Third, it develops a feedback-controlled carbon evaluation process that transforms traditional PSS from descriptive visualization tools into predictive decision-support frameworks. Applied to a representative new urban district, the framework demonstrates effectiveness in identifying low-carbon planning strategies under varying investment-intensity scenarios. By integrating economic, spatial, and environmental dimensions into a unified analytical logic, this research provides a scalable foundation for quantitative decision-making in sustainable urban transitions and contributes a theoretical model applicable to broader regional and national carbon-neutral planning frameworks.