发表机构
Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对地球系统建模中ML/AI应用的环境影响问题，提炼出围绕模型开发流程的实用检查表，并补充相关指标，以弥合可持续原则与实际研究决策的差距。
AI 中文摘要
随着机器学习（ML）和人工智能（AI）逐渐渗透到气候、天气及地球系统建模的几乎所有方面，我们有必要停下来思考，自身的设计决策对科学研究及所消耗的计算资源意味着什么。越来越多的文献探讨ML/AI的伦理与可持续发展，但将这些原则转化为日常研究实践仍是一项挑战，因为多数最佳实践分散在多项研究与评论中。在此，我们将这些讨论提炼为一份实用检查表，供ML/AI及地球系统科学从业者评估并减少自身应用的环境足迹，该检查表围绕模型开发流程的连续阶段组织。我们还补充了从文献中选取的指标，用于估算项目的能源消耗与碳足迹。针对每个问题，我们都指出了近期文献中的具体示例与可操作建议，旨在弥合理想原则与研究人员在开发周期各阶段面临的决策之间的差距。
英文摘要
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.
Comments12 pages, 1 figure, 2 tables; Submitted and presented at the GREEN-AI workshop of the ECML PKDD 2026 conference in Naples
