mlsanityData to 14 Oct 2026

AI4GCC-Team -- Below Sea Level: Score and Real World Relevance

arXiv 2307.13892 ↗First posted 26 Jul 2023cs.CY cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

As our submission for track three of the AI for Global Climate Cooperation (AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N climate-economic simulation. Our proposal seeks to address the challenges of carbon leakage through methods inspired by the Carbon Border Adjustment Mechanism (CBAM) and Climate Clubs (CC). We demonstrate the effectiveness of our approach by comparing simulated outcomes to representative concentration pathways (RCP) and shared socioeconomic pathways (SSP). Our protocol results in a temperature rise comparable to RCP 3.4/4.5 and SSP 2. Furthermore, we provide an analysis of our protocol's World Trade Organization compliance, administrative and political feasibility, and ethical concerns. We recognize that our proposal risks hurting the least developing countries, and we suggest specific corrective measures to avoid exacerbating existing inequalities, such as technology sharing and wealth redistribution. Future research should improve the RICE-N tariff mechanism and implement actions allowing for the aforementioned corrective measures.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 39 references that are in the map. First posted on arXiv 26 Jul 2023.

Builds on

2 of 39 references.

References · 2

  1. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
  2. 2022AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-Ncited by 22

Built on by

Not cited yet.