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Published VersionCC BY V4.0, Open Access
Published VersionCC BY V4.0, Open
Journal article
Artificial intelligence and climate change: the potential roles of foundation models
Environmental Sciences Europe, Vol.37, pp.1-9
2025
Abstract
Artificial intelligence (AI) is being developed fast and applied in several areas including education and healthcare with excellent potential for use in fields that require complex analytics, particularly in the case of climate change. Recent developments in AI, such as ChatGPT and OpenAI, machine vision technologies and deep learning, among others, may be deployed in various contexts, including climate change. Of specific interest is the role played by foundation models (FMs), which may help to augment intelligence on climate change and reduce the social risks of adaptation and mitigation initiatives. This article discusses the potential applications of FMs in climate change research and management and illustrates the need for further studies. FMs, built on large unlabelled data sets and enabled by transfer learning, offer versatility in handling complex tasks. Specifically, FMs can aid in climate data analysis, modelling future scenarios, assessing risks, and supporting decision-making processes. Despite their potential, challenges such as data privacy, algorithm bias, and energy consumption require careful consideration. The article emphasizes the importance of interdisciplinary efforts to address these challenges and maximize the positive impact of FMs in mitigation and adaptation. AI, including advanced models like FMs, holds significant promise for addressing climate change challenges.
Details
- Title
- Artificial intelligence and climate change: the potential roles of foundation models
- Authors
- Walter Leal Filho - HAW HamburgMarina Kovaleva (Corresponding Author) - HAW HamburgArtie W. Ng - Balsillie School of International AffairsGustavo J. Nagy - Universidad de la República de UruguayJohannes M. Lutz - University of the Sunshine Coast, Queensland, School of Law and SocietyMaria Alzira Pimenta Dinis - Fernando Pessoa University
- Publication details
- Environmental Sciences Europe, Vol.37, pp.1-9
- Publisher
- SpringerOpen
- Date published
- 2025
- DOI
- 10.1186/s12302-025-01153-2
- ISSN
- 2190-4715; 2190-4707
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Data Availability
- No datasets were generated or analysed during the current study.
- Organisation Unit
- School of Law and Society
- Language
- English
- Record Identifier
- 991185245302621
- Output Type
- Journal article
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