Explainable Machine Learning for Urban Carbon Dynamics: Mechanistic Insights and Scenario Projections in Shanghai, China

Using Shanghai as a case study, this paper estimates multi-sector urban carbon emissions by integrating multi-source statistical data from 2000 to 2023 with IPCC guidelines. Via rolling-window time-series validation, XGBoost is the most reliable model. To better understand the underlying drivers, explainable machine-learning approaches, including SHAP and the Friedman H-statistic, are applied to examine the nonlinear effects and interactions of population scale, industrial energy efficiency, investment structure, and infrastructure. The results suggest that Shanghai’s emission pattern has gradually shifted from a scale-driven process toward one dominated by structural change and efficiency improvement. Building on an incremental framework, four scenarios, Business-as-Usual, Green Transition, High Investment, and Population Plateau, are designed to simulate emission trajectories from 2024 to 2060. The simulations reveal a two-stage pattern, with a period of rapid growth followed by high-level stabilisation and a weakening path-dependence effect. Population agglomeration, economic growth, and urbanisation remain the main contributors to emission increases, while industrial upgrading and efficiency gains provide sustained mitigation over time. Scenario comparisons further indicate that only the Green Transition pathway supports early peaking, a steady decline, and long-term low-level stabilisation. Overall, this study offers a data-efficient framework for analysing urban carbon-emission dynamics and informing medium- to long-term mitigation strategies in megacities.