Adaptive Countermeasure Generation for Climate Fiscal Policy Spillovers Using Deep Reinforcement Learning and Graph Neural Networks

Against the backdrop of accelerating global climate change and advancing green transitions, national fiscal instruments deployed by countries, such as carbon taxes, green subsidies and public investment, are increasingly generating unintended cross-border spillover effects, undermining the macroeconomic stability and fiscal space of other countries through trade linkages, capital flows and regulatory arbitrage, and creating complex interdependencies that challenge traditional economic coordination. While traditional dynamic stochastic general equilibrium models and extended IS-LM frameworks provide some insight into cross-border policy interactions, their rigidity, equilibrium focus, and reliance on static assumptions prevent them from adequately capturing the adaptive, nonlinear, and high-dimensional nature of real-world fiscal dynamics. Combining advances in artificial intelligence techniques of graph neural networks and deep reinforcement learning, the study proposes a framework for generating adaptive policy responses to climate fiscal spillovers, which simulates state behavior in a multi-intelligence policy system and dynamically generates context-aware and forward-looking policy responses. A cross-country fiscal impact network is constructed by using empirical fiscal data from IMF and OECD sources, and model performance is evaluated in scenarios involving carbon tax spillovers and green subsidy competition. This study advances the learning paradigm of international macroeconomic coordination and provides a robust and scalable tool for smart climate policy governance.