Explore the possibility of advancing climate negotiations on the basis of regional trade organizations: A study based on RICE-N

TL;DR

This paper explores using deep learning-based agent models built on RICE-N to simulate climate negotiations within regional trade organizations, aiming to enhance international cooperation strategies.

Contribution

It introduces a novel approach combining deep learning and ABMs based on RICE-N to simulate climate negotiations within trade groups.

Findings

Simulation shows promising prospects for regional trade-based negotiation strategies.

Deep learning-based ABMs can effectively model complex climate negotiation dynamics.

The approach offers new theoretical support for international climate cooperation.

Abstract

Climate issues have become more and more important now. Although global governments have made some progress, we are still facing the truth that the prospect of international cooperation is not clear at present. Due to the limitations of the Integrated assessment models (IAMs) model, it is difficult to simulate the dynamic negotiation process. Therefore, using deep learning to build a new agents based model (ABM) might can provide new theoretical support for climate negotiations. Building on the RICE-N model, this work proposed an approach to climate negotiations based on existing trade groups. Simulation results show that the scheme has a good prospect.