Jump to:

• Introduction
• Scope
• Topics
• Timetable
• Submission Guidelines
• Guest Editors

Introduction 

Environmental Data Science - the peer-reviewed, open-access journal at Cambridge University Press dedicated to the interface between data science and the environmental sciences - is pleased to continue its partnership with the Fragile Earth workshop, which takes place at ACM's KDD 2025 Conference (August 3-7 2025, Toronto, Canada). 

The workshop is a recurring event. Interested parties should see the AI4Good Foundation website for more information about its history and future events.

EDS invites authors accepted into the workshop to submit their work to the journal, where it will under peer review and if accepted be added to a special collection dedicated to Fragile Earth. Authors who do not participate in the workshop but are working on the topics related to the scope are also invited to submit.

Scope

Since 2016, the Fragile Earth Workshop has brought together the research community to find and explore how data science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs). Over the years, Fragile Earth workshop has focused on SDGs. This year it is also focuses on Environmental Justice, which in the scope of scientific research can be defined as the effort to “document and redress the disproportionate environmental burdens and benefits associated with social inequalities” (Chakraborty et. al 2016), as well as SDG 13: Climate Action. 

In 2021, the Intergovernmental Panel on Climate Change (IPCC) released their report on “physical science basis” stating that climate change was caused “unequivocally” by human action, while in 2022, they followed that up with their latest report on “impacts, adaptation and vulnerability” emphasizing that the time for action is now. These reports, parts of the Sixth Assessment Report (AR6), provide new estimates of the chances of crossing global warming thresholds, discuss the urgent need for adaptation pathways, and find that unless there are immediate, rapid, and large-scale reductions in greenhouse gas emissions, limiting warming will be beyond reach. The ramifications of these climate scenarios are devastating for the planet. As we fail to reach mitigation and adaptation targets, we will witness the triggering of more frequent sweltering heat waves, stronger storms, higher floods, severe droughts, and drastic ecosystem shifts, with disastrous consequences for human lives, economies and health, as well as biodiversity. 

The application problems and agenda of interest include the Sustainable Development Goals, accelerating progress on the United Nations’ 2030 agenda, envisioning solutions for climate mitigation and adaptation, and measuring and diminishing the inequitable benefits and burdens across socioeconomic groups. In particular, the workshop has maintained a strong focus and community in the following areas: food security, sustainable agricultural practices and supply chains, ecosystem restoration, water management, sustainable energy, climate action and adaptation, socioeconomic equality, and disaster resilience. In the Fragile Earth Workshop, ML and AI researchers, social and behavioral scientists, as well as natural scientists and engineers, are invited to convene and discuss interdisciplinary solutions for progress towards the SDGs, Environmental Justice, and climate change mitigation and adaptation. 

Topics

The methodological topics of interest are relevant areas of KDD, including but not limited to:

• generative and foundational models in the context of climate change and sustainable development
• diverse datasets and knowledge integration
• secure and trustworthy generative and foundational models
• the integration of physics into data-driven modeling and the use of machine learning, generative and foundational models to enhance physical simulations
• model explainability, uncertainty quantification, privacy and fairness questions in environmental modeling
• integration of symbolic and neural machine learning for accurate and interpretable machine learning, generative and foundational models
• causal learning in complex physical world as foundations for model trustworthiness
• sustainable computing paradigms for generative and foundation models
• ML applications at low-energy edge devices
• frameworks for helping the scientific and KDD communities to work together
• combining predictive and prescriptive tasks
• multi-agent systems for participatory modeling that integrate stakeholders into knowledge creation and decision processes geometric and topological deep learning for environmental modeling and assessment of environmental justice

Domains of interest include but are not limited to:

• food security, sustainable agricultural practices and supply chains, ecosystem restoration, water management, sustainable energy, climate action and adaptation, socioeconomic equality, and disaster resilience
• wildfire analytics: detection, prediction, and discovery; wildfire smoke and environmental fairness
• innovations in data science and predictive modeling, applied to earth sciences
• investigations centering sustainability, including but not limited to environmental justice
• data-informed climate change and resource management policy discussions
• carbon removal technologies
• easily usable and publicly available data+model+frameworks (possibly challenge problems) based on satellite/drone data to monitor and predictively model the fragile earth
• natural catastrophes under a changing climate ranging from improved modeling to development of resilient infrastructures
• economic/quantitative characterization of climate change risk and associated incentives towards policy/decision making.

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 a curated page for the collection of articles.

Timetable

Key dates are below but see the AI4Good website for details pertaining to the Fragile Earth 2024 workshop

• Workshop Takes Place: August 3rd, 2025
• Submission to EDS: February 2nd, 2026

Short presentations (abstracts) accepted into Fragile Earth should be developed into full papers and will go through standard EDS peer review (two reviews). Longer presentations accepted into Fragile Earth do not need to be extended, but can be if the authors wish. They go through a streamlined review process in EDS (one additional review). 

Earlier submission is encouraged and may result in earlier publication. Articles will be added to the Fragile Earth special collection page on EDS.

Abstracts would need to be developed into full papers and go through standard EDS peer review, but extended articles accepted into Fragile Earth would go through a streamlined review process in EDS (just one reviewer). 

Submission Guidelines

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, Overleaf 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 

Version and Marked Up Manuscript: Authors of full papers from Fragile Earth 2025 are welcome to either submit the paper as presented at the workshop or an updated version. We'd appreciate it if authors provide a marked-up manuscript indicating the changes to the workshop version. This will aid the reviewers. 

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. 

Guest Editors

• Yuzhou Chen (University of California Riverside, USA)
• Emre Eftelioglu (Amazon, USA)