Global benefits from climate mitigation can be realised only through cooperative mechanisms that provide incentives for all countries. A regime integrating carbon tariffs and transfer payments can leverage such cooperation by providing incentives for individual countries’ mitigation efforts; however, it faces methodological challenges arising from the interdependence and instability of national strategies. This study develops a computable general equilibrium–agent-based model (CGE–ABM) framework that links global macroeconomic mechanisms with national strategic decision-making. Reinforcement learning agents identify equilibrium coalitions using a payoff matrix derived from 8192 CGE simulations that exhaustively represent regional cooperation combinations across four mitigation targets, with the European Union as the fixed initiator. Simulations indicate that the 2 °C goal is attainable if major emitters—particularly developed economies—jointly initiate cooperation. Active initiation of major developed economies can help expand participation, raising base-year emission coverage from 54% to 87% and boosting cumulative emission reductions by 2060 from 10% to as high as 19%. Transfer payments funded by the incremental economic gains of the initiators are the key to leveraging global participation and represent Pareto improvements for all countries; however, the 1.5 °C target imposes prohibitive costs, especially on developing countries, limiting coalition expansion. Early actions—such as complete implementation of the NDCs—can effectively lower the barriers to global collaboration. The hybrid modelling framework deepens our understanding of the 2 °C climate goal and identifies pathways to it, providing actionable insights for countries seeking to participate in global climate mitigation efforts.
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Zijian Chen
Zhejiang Chinese Medical University
Wei-Qi TANG
Fudan University
Yu-Shi WANG
Fudan University
Advances in Climate Change Research
Fudan University
Chinese Academy of Meteorological Sciences
Shanghai Academy of Environmental Sciences
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Chen et al. (Wed,) studied this question.
synapsesocial.com/papers/69e31ff140886becb653efff — DOI: https://doi.org/10.1016/j.accre.2026.04.002