Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead

• URL: http://arxiv.org/abs/2506.09683v1
• Date: Wed, 11 Jun 2025 13:02:00 GMT
• Title: Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
• Authors: Priyavanshi Pathania, Nikhil Bamby, Rohit Mehra, Samarth Sikand, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. Burden,
• Abstract summary: The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint.<n>We present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions.
• Score: 8.377809633825196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, understanding and optimizing how software impacts the environment becomes paramount. In this paper, we present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions. We introduce a taxonomy to categorize the existing work as Monitoring, Estimation, or Black-Box approaches. We delve deeper into the tools and compare them across different dimensions and granularity - for example, whether their measurement encompasses energy and carbon emissions and the components considered (like CPU, GPU, RAM, etc.). We present our observations on the practical use (component wise consolidation of approaches) as well as the challenges that we have identified across the current state-of-the-art. As we start an initiative to address these challenges, we emphasize active collaboration across the community in this important field.

Related papers

• WattsOnAI: Measuring, Analyzing, and Visualizing Energy and Carbon Footprint of AI Workloads [8.545822371190125]
  WattsOnAI is a comprehensive software toolkit for the measurement, analysis, and visualization of energy use, power draw, hardware performance, and carbon emissions across AI workloads.<n>By seamlessly integrating with existing AI frameworks, WattsOnAI offers standardized reports and exports fine-grained time-series data.<n>WattsOnAI encourages the research community to weigh environmental impact alongside raw performance of AI workloads and advances the shift toward more sustainable "Green AI" practices.
  arXiv  Detail & Related papers  (2025-06-25T15:24:45Z)
• Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs [8.377809633825196]
  We discuss the challenges of current approaches and present our evolving framework, R-ICE, which estimates prompt level inference carbon emissions.<n>Our promising validation results suggest that benchmark-based modelling holds great potential for inference emission estimation.
  arXiv  Detail & Related papers  (2025-06-10T12:23:02Z)
• From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
  We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.<n>We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions.
  arXiv  Detail & Related papers  (2025-01-27T22:45:06Z)
• A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning [0.5224038339798622]
  This paper introduces the main considerations necessary for making sound use of energy measurement tools.<n>It includes the use of at-the-wall versus on-device measurements, sampling strategies and best practices, common sources of error, and proxy measures.<n>It concludes with a call to action for improving the state of the art of measurement methods.
  arXiv  Detail & Related papers  (2024-12-11T19:00:00Z)
• Carbon-Efficient Software Design and Development: A Systematic Literature Review [1.6071754144962787]
  We conduct a systematic literature review on state-of-the-art proposals for designing and developing carbon-efficient software.<n>We identify and analyse 65 primary studies by classifying them through a taxonomy aimed at answering the 5W1H questions of carbon-efficient software design and development.
  arXiv  Detail & Related papers  (2024-07-29T11:24:11Z)
• Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
  quantifying the energy footprint of a software system is one of the most basic activities. This document aims to be a starting point for researchers who want to begin conducting work in this area.
  arXiv  Detail & Related papers  (2024-07-16T11:21:30Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning [77.62876532784759]
  Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
  arXiv  Detail & Related papers  (2023-02-16T18:35:00Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)
• Sustainable AI: Environmental Implications, Challenges and Opportunities [13.089123643565724]
  We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases. We present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI.
  arXiv  Detail & Related papers  (2021-10-30T23:36:10Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.