Green Algorithms: Quantifying the Carbon Footprint of Computation

By Loïc Lannelongue, Jason Grealey, Michael Inouye. 2020

522 citations recorded by the source corpus.

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 (GHG) emissions, including data centers and other sources of large‐scale computation. Although many important scientific milestones are achieved thanks to the development of high‐performance computing, the resultant environmental impact is underappreciated. In this work, a methodological framework to estimate the carbon footprint of any computational task in a standardized and reliable way is presented and metrics to contextualize GHG emissions are defined. A freely available online tool, Green Algorithms (www.green‐algorithms.org) is developed, which enables a user to estimate and report the carbon footprint of their computation. The 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 hardware configurations. Finally, the GHG emissions of algorithms used for particle physics simulations, weather forecasts, and natural language processing are quantified. Taken together, this study develops a simple generalizable framework and freely available tool to quantify the carbon footprint of nearly any computation. Combined with recommendations to minimize unnecessary CO2 emissions, the authors hope to raise awareness and facilitate greener computation.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems, Scaling & Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. David A. Patterson
2. Ben Glocker
3. Erik H. W. Meijering
4. Gary S. Collins
5. Karel G. M. Moons
6. Quoc V. Le
7. Gaël Varoquaux
8. Joseph Dean

In Electrical and Electronic Engineering

1. Chen Liang
2. David A. Patterson
3. Ben Glocker
4. Quoc V. Le
5. David R. So
6. Joseph E. Gonzalez
7. Daniel Rothchild
8. Wenhu Chen

In Information Systems

1. Chen Liang
2. David A. Patterson
3. Gary S. Collins
4. Veronika Cheplygina
5. Quoc V. Le
6. Gaël Varoquaux
7. Joseph Dean
8. Joseph E. Gonzalez

In Scaling & Learning Theory

1. Quoc V. Le
2. Anne L. Martel
3. Joseph E. Gonzalez
4. B. van Ginneken

Citations in the corpus

• Carbon Emissions and Large Neural Network Training (2021, methodology): “Without access to such a dashboard, use the ML Emissions Calculator [Lac19] or Green Algorithms tool [Lan20] that estimate the CO 2 e mix by region (see Figure 6 below) 11 .”
• Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools (2021, unknown): “The CO 2 equivalent emissions obtained from ML CO2 Impact and Green Algorithms are higher than those returned by other tools.”
• Metrics Reloaded: Recommendations for image analysis validation (2022, methodology): “For these considerations, we refer the reader to available tools such as the Green Algorithms calculator 47 or Carbontracker 48 .”
• ModernVBERT: Towards Smaller Visual Document Retrievers (2025, unknown): “This estimate follows methodologies such as Green Algorithms (Lannelongue et al., 2021) and related analyses of the carbon footprint of machine learning (Strubell et al., 2019; Patterson et al., 2021).”
• The Price of Prompting: Profiling Energy Use in Large Language Models Inference (2024, methodology): “Tools such as Green Algorithms [16] provide estimations of carbon emissions for computational tasks and yet lack real-time monitoring capabilities.”