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Environment

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

Filippo Dainelli, Amirpasha Mozaffari

Featured September 8, 2026

Original Paper

AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

To help scientists make AI for climate models more eco-friendly, this paper offers a simple checklist and measurement tools to track and reduce the energy and carbon used from start to finish.

In depth
The paper addresses the growing environmental footprint of ML/AI in Earth System Modeling by providing a structured checklist and relevant metrics. This helps researchers make informed decisions at each project stage, from scoping to deployment, to reduce energy consumption and carbon emissions.

Key Takeaways

  • 1
    A stage-based checklist guides ML/AI practitioners in Earth System Modeling to consider environmental impacts at each project phase.
  • 2
    The paper provides a curated set of metrics (e.g., FLOPs, kWh, carbon intensity) to quantify and report the environmental footprint.
  • 3
    It emphasizes "Green AI" principles, advocating for computational efficiency alongside accuracy to reduce the ecological impact of ESM applications.

Conceptual Flow

HIGH LEVEL
1
Methodology: Guiding Green AI Development

The paper gives scientists a step-by-step guide and tools to measure how much energy their computer models use, helping them build greener AI.

AI Project Idea
Follow Checklist & Measure
Greener AI Model
2
Results: Reducing Environmental Impact

By using the checklist, scientists can make choices that significantly lower the carbon footprint of their AI models, making them more sustainable.

High Energy Use
Apply Green AI Checklist
Lower Carbon Footprint

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