Explore the possibility of advancing climate negotiations on the basis of regional trade organizations: A study based on RICE-N
This work addresses the problem of unclear international cooperation prospects in climate negotiations for policymakers and researchers, but it appears incremental as it builds on existing models without major breakthroughs.
The study tackled the challenge of simulating dynamic climate negotiations by proposing a new agent-based model using deep learning, building on the RICE-N model and focusing on regional trade organizations; simulation results indicated the scheme has good prospects, though no concrete numbers were provided.
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.