Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies and health. Various human activities are responsible for significant greenhouse gas emissions, including data centres and other sources of large-scale computation. Although many important scientific milestones have been achieved thanks to the development of high-performance computing, the resultant environmental impact has been underappreciated. In this paper, we present a methodological framework to estimate the carbon footprint of any computational task in a standardised and reliable way, based on the processing time, type of computing cores, memory available and the efficiency and location of the computing facility. Metrics to interpret and contextualise greenhouse gas emissions are defined, including the equivalent distance travelled by car or plane as well as the number of tree-months necessary for carbon sequestration. We develop a freely available online tool, Green Algorithms (www.green-algorithms.org), which enables a user to estimate and report the carbon footprint of their computation. The Green Algorithms tool easily integrates with computational processes as it requires minimal information and does not interfere with existing code, while also accounting for a broad range of CPUs, GPUs, cloud computing, local servers and desktop computers. Finally, by applying Green Algorithms, we quantify the greenhouse gas emissions of algorithms used for particle physics simulations, weather forecasts and natural language processing. Taken together, this study develops a simple generalisable framework and freely available tool to quantify the carbon footprint of nearly any computation. Combined with a series of recommendations to minimise unnecessary CO2 emissions, we hope to raise awareness and facilitate greener computation.
Carbon Footprint Evaluation of Code Generation through LLM as a Service
TWT GmbH Science & Innovation（TWT科学与创新有限公司）；Harvard University（哈佛大学）；MIT（麻省理工学院）；Mercedes-Benz（梅赛德斯-奔驰）；National Technical University of Athens（雅典国家技术大学）
针对数据中心碳排放增加问题，本研究引入LLM as a Service，使用生成式AI模型GitHub Copilot自动生成代码，通过可持续性指标量化其隐含和运营碳排放，为绿色编码和AI驱动的效率在汽车等行业的应用提供评估方法。
