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New checklist guides AI/ML environmental impact assessment in Earth system modeling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a checklist to help practitioners in machine learning and artificial intelligence assess and mitigate the environmental impact of their applications within Earth system modeling. This checklist distills existing literature on ethical and sustainable AI development into actionable steps for researchers. It is organized according to the model development pipeline and includes metrics for estimating energy consumption and carbon footprint, aiming to bridge the gap between principles and practical research decisions. AI

IMPACT Provides a framework for researchers to reduce the environmental footprint of AI/ML in scientific modeling.

RANK_REASON The item is a research paper published on arXiv detailing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New checklist guides AI/ML environmental impact assessment in Earth system modeling

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The item is a research paper published on arXiv detailing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Filippo Dainelli, Amirpasha Mozaffari, Marina Casta\~no, Aina Gaya i \`Avila, Llu\'is Palma Garcia, Alessio Melli, Oscar Dimdore Miles, Amanda Duarte ·

    A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling

    arXiv:2609.00847v1 Announce Type: cross 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 co…

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