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

Yangtao Ou

doi:10.54254/2753-8818/2025.25555

Theoretical and Natural ScienceOpen access

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

Research Article

Open Access

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

Yangtao Ou ^1*

¹ Utah State University, Logan, USA

^*Corresponding author: rara481846778@gmail.com

Published on 30 July 2025

TNS Vol.134

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-307-9

ISBN (Online): 978-1-80590-308-6

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Abstract

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.

Keywords:

Climate Fiscal Policy, Spillovers, Deep Reinforcement Learning, Graph Neural Networks, Policy Coordination

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References

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References

[1]. Yang, Tianpei, et al. "A transfer approach using graph neural networks in deep reinforcement learning." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 15. 2024.

[2]. Tam, Prohim, et al. "A survey of intelligent end-to-end networking solutions: Integrating graph neural networks and deep reinforcement learning approaches." Electronics 13.5 (2024): 994.

[4]. Zhao, Jianyu, and Chenyang Yang. "Graph Reinforcement Learning for Radio Resource Allocation." arXiv preprint arXiv: 2203.03906 (2022).

[5]. Mu, Tongzhou, et al. "Learning two-step hybrid policy for graph-based interpretable reinforcement learning." arXiv preprint arXiv: 2201.08520 (2022).

[7]. Gammelli, Daniele, et al. "Graph neural network reinforcement learning for autonomous mobility-on-demand systems." 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021.

[11]. Ajagekar, Akshay, and Fengqi You. "Deep reinforcement learning based automatic control in semi-closed greenhouse systems." IFAC-PapersOnLine 55.7 (2022): 406-411

Cite this article

Ou,Y. (2025). Adaptive Countermeasure Generation for Climate Fiscal Policy Spillovers Using Deep Reinforcement Learning and Graph Neural Networks. Theoretical and Natural Science,134,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: The 3rd International Conference on Applied Physics and Mathematical Modeling

ISBN: 978-1-80590-307-9(Print) / 978-1-80590-308-6(Online)

Editor: Marwan Omar

Conference website: https://2025.confapmm.org/

Conference date: 31 October 2025

Series: Theoretical and Natural Science

Volume number: Vol.134

ISSN: 2753-8818(Print) / 2753-8826(Online)

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