This paper introduces a comprehensive Climate-gOvernance Modeling & Policy ASsessment System (COMPASS) developed by the School of Ecology & Environment, Renmin University of China. The COMPASS includes a top−down computable general equilibrium model (CE3-CGE), a bottom−up energy technology model (PECE), a localized integrated assessment model (GCAM-CHN), as well as econometric and machine learning approaches. Together, these models simulate multi-scale interactions across energy systems, technological details, and macroeconomic dynamics, supporting policy analysis ranging from macrostructural trends to micro-level technoeconomic features, and from long-term pathways to short-term fluctuations. The COMPASS could provide robust scientific support for China’s carbon neutrality governance and contribute methodological advances and scenario-based insights to the broader field of global climate policy research.

Economic-based trade patterns can result in an imbalance between environmental costs and economic benefits, and this mismatch between regional emission rights and development rights leads to trade-driven carbon inequality. Current approaches lack dynamic indicators to capture the temporal evolution of trade-related carbon inequality and to provide targeted policy guidance. This study employs an environmentally extended multi-regional input‒output model to assess carbon inequality in inter-provincial trade across China. Importantly, we extend the Tapio decoupling theory to introduce the Tapio Carbon Inequality Index (Tapio-Ine) and a novel framework for evaluating inter-temporal changes in carbon inequality. Based on trend evolution, all provinces are categorized into four groups and six dynamic states, with five provinces identified as bearing asymmetric carbon burdens. By distinguishing between investment- and consumption-driven demands, as well as production- and consumption-based carbon intensities, and applying Structural Decomposition Analysis (SDA), we identify the key drivers of provincial carbon inequality. Our findings indicate that carbon inequality is intensifying in Liaoning, Shandong and Gansu, with Xinjiang experiencing the most pronounced deterioration. Provinces exhibiting worsening inequality can adopt carbon footprint labeling to limit carbon-intensive exports and promote the development of high-value-added industries tailored to local resource endowments. Meanwhile, developed provinces should mitigate consumption-based emissions by promoting carbon credits, issuing green consumption vouchers, and implementing green public procurement policies. Provinces contributing to carbon inequality should also share best practices in low-carbon production with trading partners to support the decarbonization of inter-regional supply chains.