A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling
Abstract
As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth pausing to consider what our design decisions imply for the science and for the computational resources we consume. A growing body of literature addresses the ethical and sustainable development of ML/AI, yet translating these principles into day-to-day research practice remains a challenge as most of best practices are dispersed across multiple studies and commentaries. Here, we distill these discussions into a practical checklist that ML/AI and Earth system science practitioners can use to assess and reduce the environmental footprint of their own applications, organised around the successive stages of the model development pipeline. We complement the checklist with a selection of metrics drawn from the literature for estimating the energy consumption and carbon footprint of a project. For each question, we point to concrete examples and actionable suggestions from recent literature, aiming to bridge the gap between aspirational principles and the decisions researchers face at every stage of the development cycle.
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BibTeX
@misc{dainelli2026checklist,
title = {{A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling}},
author = {Filippo Dainelli and Amirpasha Mozaffari and Marina Castaño and Aina Gaya i Àvila and Lluís Palma Garcia and Alessio Melli and Oscar Dimdore Miles and Amanda Duarte},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.00847},
eprint = {2609.00847},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.00847},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling
AU - Filippo Dainelli
AU - Amirpasha Mozaffari
AU - Marina Castaño
AU - Aina Gaya i Àvila
AU - Lluís Palma Garcia
AU - Alessio Melli
AU - Oscar Dimdore Miles
AU - Amanda Duarte
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.00847
UR - https://arxiv.org/abs/2609.00847
AB - As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth pausing to consider what our design decisions imply for the science and for the computational resources we consume. A growing body of literature addresses the ethical and sustainable development of ML/AI, yet translating these principles into day-to-day research practice remains a challenge as most of best practices are dispersed across multiple studies and commentaries. Here, we distill these discussions into a practical checklist that ML/AI and Earth system science practitioners can use to assess and reduce the environmental footprint of their own applications, organised around the successive stages of the model development pipeline. We complement the checklist with a selection of metrics drawn from the literature for estimating the energy consumption and carbon footprint of a project. For each question, we point to concrete examples and actionable suggestions from recent literature, aiming to bridge the gap between aspirational principles and the decisions researchers face at every stage of the development cycle.
ER -