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Introduction China’s Peak Carbon and Carbon Neutral Program is a key national policy to accelerate green development and establish a foundation for high-quality, sustainable growth. Transitioning toward a low-carbon economy requires gradually reducing coal consumption while ensuring energy security. This study examines how carbon emission trading policy influence energy consumption structure and their underlying mechanisms, in order to provide theoretical and policy support for China’s green transition and high-quality development. Methods Leveraging provincial-level panel data covering 30 jurisdictions in China from 2004 to 2021, we first quantify the average causal impact of the Carbon Emission Trading Policy (CETP) on energy-structure decarbonization through a two-way fixed-effects difference-in-differences (DID) estimator. Recognizing that linear identification strategies are ill-suited to recover heterogeneous treatment thresholds, regime-dependent mediation channels, or discrete structural breaks, we complement the DID backbone with non-parametric Classification and Regression Trees (CART) to reveal latent regime-specific policy functions and higher-order interaction effects. Results (1) The CETP significantly reduces carbon emission intensity and optimises the energy structure, demonstrating its dual effectiveness in emission reduction and transformation. (2) Technological innovation and industrial restructuring are the main transmission channels, through which the policy fosters a synergistic “technology–industry” dynamic by promoting green RD (research and development) and curbing high-carbon sectors. (3) Urbanisation level critically shapes policy effectiveness, with highly urbanised regions exhibiting stronger transition outcomes due to economies of scale and institutional advantages. Notably, municipalities outperform other pilot provinces, indicating substantial interregional heterogeneity. Innovation This study breaks through the traditional single-path analysis framework by integrating multi-dimensional transmission mechanisms and constructing an urbanisation regulation model to reveal the economic geography logic of regional heterogeneity, while integrating econometrics and machine learning methods to enhance the robustness of the conclusions. The study provides theoretical and empirical support for the optimisation of carbon market design, the formulation of differentiated regional policies, and the acceleration of the ‘dual-carbon’ goal.
Zhao et al. (Mon,) studied this question.