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

• URL: http://arxiv.org/abs/2307.14226v1
• Date: Wed, 26 Jul 2023 14:48:25 GMT
• Title: Explore the possibility of advancing climate negotiations on the basis of regional trade organizations: A study based on RICE-N
• Authors: Wubo Dai
• Abstract summary: Building on the RICE-N model, this work proposed an approach to climate negotiations based on existing trade groups. Deep learning could provide new theoretical support for climate negotiations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM [77.17254959695218]
  Large Language Models (LLMs) like ChatGPT and Bard have shown impressive conversational abilities and excel in a wide variety of NLP tasks. We propose a light-weight Arabic Mini-ClimateGPT that is built on an open-source LLM and is specifically fine-tuned on a conversational-style instruction tuning Arabic dataset Clima500-Instruct. Our model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation.
  arXiv  Detail & Related papers  (2023-12-14T22:04:07Z)
• Improving International Climate Policy via Mutually Conditional Binding Commitments [0.0]
  This paper proposes enhancements to the RICE-N simulation and multi-agent reinforcement learning framework. We highlight the necessity of significant enhancements to address the diverse array of factors in modeling climate negotiations.
  arXiv  Detail & Related papers  (2023-07-26T15:53:21Z)
• Dynamic Grouping for Climate Change Negotiation: Facilitating Cooperation and Balancing Interests through Effective Strategies [9.724269599288748]
  We develop a three-stage process: group formation and updates, intra-group negotiation, and inter-group negotiation. Our model promotes efficient and effective cooperation between various stakeholders to achieve global climate change objectives. We demonstrate our negotiation model within the RICE-N framework, illustrating a promising approach for facilitating international cooperation on climate change mitigation.
  arXiv  Detail & Related papers  (2023-07-26T01:34:43Z)
• AI For Global Climate Cooperation 2023 Competition Proceedings [77.07135605362795]
  No global authority can ensure compliance with international climate agreements. RICE-N supports modeling regional decision-making using AI agents. The IAM then models the climate-economic impact of those decisions into the future.
  arXiv  Detail & Related papers  (2023-07-10T20:05:42Z)
• AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N [75.67460895629348]
  Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. We introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy. We describe how to use multi-agent reinforcement learning to train rational agents using RICE-N.
  arXiv  Detail & Related papers  (2022-08-15T04:38:06Z)
• Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark [95.19070157520633]
  Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks. Such models, recently coined as foundation models, have been transformational to the field of natural language processing. We propose to develop a new benchmark comprised of a variety of downstream tasks related to climate change.
  arXiv  Detail & Related papers  (2021-12-01T15:38:19Z)
• CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims [4.574830585715129]
  We introduce CLIMATE-FEVER, a new dataset for verification of climate change-related claims. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the textscfever framework.
  arXiv  Detail & Related papers  (2020-12-01T16:32:54Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.