AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N

Tianyu Zhang, Andrew Robert Williams, Phillip Wozny, Kai-Hendrik Cohrs, Koen Ponse, Marco Jiralerspong, Soham Rajesh Phade, Sunil Srinivasa, Lu Li, Yang Zhang, Prateek Gupta, Erman Acar, Irina Rish, Yoshua Bengio, Stephan Zheng
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:76332-76360, 2025.

Abstract

Global cooperation on climate change mitigation is essential to limit temperature increases while supporting long-term, equitable economic growth and sustainable development. Achieving such cooperation among diverse regions, each with different incentives, in a dynamic environment shaped by complex geopolitical and economic factors, without a central authority, is a profoundly challenging game-theoretic problem. This article introduces RICE-N, a multi-region integrated assessment model that simulates the global climate, economy, and climate negotiations and agreements. RICE-N uses multi-agent reinforcement learning (MARL) to encourage agents to develop strategic behaviors based on the environmental dynamics and the actions of the others. We present two negotiation protocols: (1) Bilateral Negotiation, an exemplary protocol and (2) Basic Club, inspired from Climate Clubs and the carbon border adjustment mechanism (Nordhaus, 2015; Comissions, 2022). We compare their impact against a no-negotiation baseline with various mitigation strategies, showing that both protocols significantly reduce temperature growth at the cost of a minor drop in production while ensuring a more equitable distribution of the emission reduction costs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-zhang25ce, title = {{AI} for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in {RICE}-N}, author = {Zhang, Tianyu and Williams, Andrew Robert and Wozny, Phillip and Cohrs, Kai-Hendrik and Ponse, Koen and Jiralerspong, Marco and Phade, Soham Rajesh and Srinivasa, Sunil and Li, Lu and Zhang, Yang and Gupta, Prateek and Acar, Erman and Rish, Irina and Bengio, Yoshua and Zheng, Stephan}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {76332--76360}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/zhang25ce/zhang25ce.pdf}, url = {https://proceedings.mlr.press/v267/zhang25ce.html}, abstract = {Global cooperation on climate change mitigation is essential to limit temperature increases while supporting long-term, equitable economic growth and sustainable development. Achieving such cooperation among diverse regions, each with different incentives, in a dynamic environment shaped by complex geopolitical and economic factors, without a central authority, is a profoundly challenging game-theoretic problem. This article introduces RICE-N, a multi-region integrated assessment model that simulates the global climate, economy, and climate negotiations and agreements. RICE-N uses multi-agent reinforcement learning (MARL) to encourage agents to develop strategic behaviors based on the environmental dynamics and the actions of the others. We present two negotiation protocols: (1) Bilateral Negotiation, an exemplary protocol and (2) Basic Club, inspired from Climate Clubs and the carbon border adjustment mechanism (Nordhaus, 2015; Comissions, 2022). We compare their impact against a no-negotiation baseline with various mitigation strategies, showing that both protocols significantly reduce temperature growth at the cost of a minor drop in production while ensuring a more equitable distribution of the emission reduction costs.} }
Endnote
%0 Conference Paper %T AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N %A Tianyu Zhang %A Andrew Robert Williams %A Phillip Wozny %A Kai-Hendrik Cohrs %A Koen Ponse %A Marco Jiralerspong %A Soham Rajesh Phade %A Sunil Srinivasa %A Lu Li %A Yang Zhang %A Prateek Gupta %A Erman Acar %A Irina Rish %A Yoshua Bengio %A Stephan Zheng %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-zhang25ce %I PMLR %P 76332--76360 %U https://proceedings.mlr.press/v267/zhang25ce.html %V 267 %X Global cooperation on climate change mitigation is essential to limit temperature increases while supporting long-term, equitable economic growth and sustainable development. Achieving such cooperation among diverse regions, each with different incentives, in a dynamic environment shaped by complex geopolitical and economic factors, without a central authority, is a profoundly challenging game-theoretic problem. This article introduces RICE-N, a multi-region integrated assessment model that simulates the global climate, economy, and climate negotiations and agreements. RICE-N uses multi-agent reinforcement learning (MARL) to encourage agents to develop strategic behaviors based on the environmental dynamics and the actions of the others. We present two negotiation protocols: (1) Bilateral Negotiation, an exemplary protocol and (2) Basic Club, inspired from Climate Clubs and the carbon border adjustment mechanism (Nordhaus, 2015; Comissions, 2022). We compare their impact against a no-negotiation baseline with various mitigation strategies, showing that both protocols significantly reduce temperature growth at the cost of a minor drop in production while ensuring a more equitable distribution of the emission reduction costs.
APA
Zhang, T., Williams, A.R., Wozny, P., Cohrs, K., Ponse, K., Jiralerspong, M., Phade, S.R., Srinivasa, S., Li, L., Zhang, Y., Gupta, P., Acar, E., Rish, I., Bengio, Y. & Zheng, S.. (2025). AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:76332-76360 Available from https://proceedings.mlr.press/v267/zhang25ce.html.

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