mlsanityData to 14 Oct 2026

Chasing Carbon: The Elusive Environmental Footprint of Computing

arXiv 2011.02839 ↗First posted 28 Oct 2020cs.AR cs.CYcited by 72

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Abstract (arXiv)

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems.

Key facts. Cited by 72 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Oct 2020.

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Citing papers per year: 2021: 6; 2022: 12; 2023: 12; 2024: 14; 2025: 16; 2026: 12.

72 papers.

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  5. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
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  8. 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 92
  9. 2023Data Distillation: A Surveycited by 87
  10. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  11. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 72
  12. 2024Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inferencecited by 61
38 more
  1. 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
  2. 2022Compute-Efficient Deep Learning: Algorithmic Trends and Opportunitiescited by 38
  3. 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34
  4. 2021Unraveling the Hidden Environmental Impacts of AI Solutions for Environmentcited by 32
  5. 2025Measuring the environmental impact of delivering AI at Google Scalecited by 32
  6. 2024Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU modelcited by 18
  7. 2023MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systemscited by 14
  8. 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inferencecited by 13
  9. 2025Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scalingcited by 13
  10. 2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal controlcited by 12
  11. 2022Treehouse: A Case For Carbon-Aware Datacenter Softwarecited by 12
  12. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  13. 2023Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Sciencecited by 11
  14. 2021Dissecting FLOPs along input dimensions for GreenAI cost estimationscited by 10
  15. 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
  16. 2025Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trendscited by 10
  17. 2024Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigationcited by 9
  18. 2022On Sampling Collaborative Filtering Datasetscited by 8
  19. 2024Beyond Efficiency: Scaling AI Sustainablycited by 8
  20. 2025CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimizationcited by 7
  21. 2022Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarkscited by 5
  22. 2023PolyThrottle: Energy-efficient Neural Network Inference on Edge Devicescited by 5
  23. 2022Sustainable Computing -- Without the Hot Aircited by 4
  24. 2022Combining Compressions for Multiplicative Size Scaling on Natural Language Taskscited by 4
  25. 2023DADO -- Low-Cost Query Strategies for Deep Active Design Optimizationcited by 4
  26. 2022Position: Tensor Networks are a Valuable Asset for Green AIcited by 3
  27. 2025Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Modelscited by 3
  28. 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demandcited by 2
  29. 2024Energy Consumption Trends in Sound Event Detection Systemscited by 2
  30. 2024e-Fold Cross-Validation for Recommender-System Evaluationcited by 2
  31. 2024A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communitiescited by 2
  32. 2024How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impactscited by 2
  33. 2025Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Serverscited by 2
  34. 2025The Carbon Cost of Conversation, Sustainability in the Age of Language Modelscited by 2
  35. 2025MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessingcited by 2
  36. 2025The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Reboundcited by 2
  37. 2024Photonics for Sustainable Computingcited by 1
  38. 2025Comparing energy consumption and accuracy in text classification inferencecited by 1

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