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• Introduction
• Scope
• Topics
• Timetable
• Submission guidelines
• Open access
• Open materials
• Guest Editors

Introduction 

Cambridge University Press is pleased to be collaborating with Climate Change AI - a global initiative to catalyse impactful work at the intersection of climate change and machine learning – on the following Call for Papers that will lead to a special collection in Environmental Data Science (EDS). This is linked to the Tackling Climate Change with Machine Learning workshops that take place at ICLR and NeurIPS conferences. We encourage submissions deriving from these events but we are also open to any relevant submission from authors not taking part in these workshops.

Scope

This Special Collection will focus on the use of artificial intelligence (AI) and machine learning (ML) to help address climate change, encompassing mitigation efforts (reducing greenhouse gas emissions), adaptation measures (preparing for unavoidable consequences), and climate science (our understanding of the climate and future climate predictions).

Specifically, we aim to: (1) showcase high-impact applications of AI/ML to climate change mitigation, adaptation, and climate science, (2) discuss related research directions to which the AI/ML community can contribute, (3) brainstorm mechanisms to scale early academic research to successful, viable deployments, and (4) encourage fruitful collaboration between the AI/ML community and a diverse set of researchers and practitioners from climate change-related fields.

Topics

The Special Collection is expected to feature climate-relevant applications of machine learning to a wide variety of sectors and topics, including:

Climate related topics:

Agriculture and food; behavioral and social science; buildings and transportation; carbon capture and sequestration; cities and urban planning; climate finance; climate justice; climate policy; climate science and modeling; disaster management and relief; earth observations and monitoring; earth science; ecosystems and biodiversity; extreme weather; forestry and land use; health; heavy industry and manufacturing; local and indigenous knowledge systems; materials science and discovery; oceans and marine systems; power and energy systems; public policy; societal adaptation and resilience; supply chains; transportation

ML-related topics:

Active learning; causal and bayesian methods; classification, regression, and supervised learning; computer vision and remote sensing; data mining; generative modeling; hybrid physical models; interpretable ML; meta and transfer learning; NLP; recommender systems; reinforcement learning and control; time series analysis; uncertainty quantification and robustness; unsupervised and semi-supervised learning.

Metrics/Impact related topics: 

As AI/ML are increasingly used for climate action, the ultimate efficacy and deployability of the proposed methods will depend on the quality of the metrics that are used to develop them. However, there is often a divergence between the metrics used by AI/ML practitioners and the metrics valued in the climate-relevant domains in which AI/ML algorithms are meant to be deployed. For example, while there is much research on safe and robust machine learning (e.g., in the areas of safe reinforcement learning and adversarially robust deep learning), the notions of “safety” and “robustness” used within AI/ML often differ from the notions used within power grids (which stem from, e.g., electrical engineering and control theory); this serves to potentially hamper the deployment of ML within power grid decarbonization workflows.

In addition, as AI/ML methods are deployed for climate action (and more broadly), it is important to understand what their impacts on climate change mitigation, adaptation, and climate equity actually are; however, there is a lack of proper metrics and impact assessment frameworks to evaluate this in practice. For instance, practitioners may want to quantify the greenhouse gas emission savings and increases resulting from not only the computational energy used for the model but also from how it is applied in a particular setting; however such evaluation metrics and tools are only beginning to be developed.

Timetable 

ICLR 2025:

• Climate Change AI workshop takes: April 24-28, 2025
• EDS submission deadline for ICLR authors (suggested): Authors welcome to submit as soon as they are ready (final deadline: 31 October 2025 earlier submission encouraged)

Articles will be published as soon as possible after acceptance, in the interest of allowing authors to disseminate their work without unnecessary delay, and added to this Tackling Climate Change with Machine Learning collection page. 

Submission guidelines

Please note the following key details, with more information available in the EDS Instructions for Authors:

Article Types: We assume full papers accepted into the workshop will either be submitted to EDS as application or methods papers, with authors of position papers and policy notes for the workshop invited to submit either position papers or perspectives. A full list of article types is here.

Templates: EDS LaTeX and Word templates are available but authors are not required to use these. Authors using the ACM template will need to adapt the article to include: 

• Impact Statement: 120 words beneath the abstract describing the significance of the findings in language that can be understood by a wide audience
• Author contributions (using the CRedIT taxonomy as a guide)
• Competing interest statement
• Data availability statement
• Funding statement

See the EDS Instructions for Authors for more details about these statements. Note that we strongly encourage authors to make replication code and data available via open repositories, which should be linked to in the Data Availability Statement. Authors doing so will be awarded Open Data and Open Materials badges on publication.

Transparent peer review: Note that EDS has a transparent peer review model. Reviews are published alongside the article, if accepted, and reviewers have the option to identify themselves or remain anonymous in the published report. For more details read about the process here. 

Open access

Any author can publish on an open access basis in EDS if accepted, irrespective of their funding situation or institutional affiliation. There are no financial barriers to publication. Many articles have publishing costs covered through the Transformative Agreements Cambridge has set up with universities worldwide. If the corresponding author on an article is affiliated with a Transformative Agreement this effectively covers publishing costs. Authors not affiliated with these agreements who have received a grant that budgets for open access publication are encouraged to pay an article processing charge (APC). However, if an author has no funding and no institutional agreement, the charge will be waived. Please do not let concerns about your financial situation or affiliation put off your submission.

Open materials

Authors are encouraged to make code and data that supports the findings openly available in a recognised repository and to link to them in the Data Availability Statement in the article. We recognise this may not be possible in all circumstances. See the EDS Research Transparency policy. Open Data and Open Materials badges will be displayed on published articles that link to replication materials, as a recognition of open practices.

Guest Editors

• Joachim Denzler (Friedrich Schiller University Jena)
• Patrick Emami (National Renewable Energy Laboratory)
• Emily Gordon (Stanford University)
• Panayiotis Moutis (City College New York)
• Zoltan Nagy (University of Texas Austin)
• Tejasri Nampally (Climate Change AI)
• Mark Roth (Climate Change AI)