Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912575270158336 |
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| author | Pathania, Priyavanshi Bamby, Nikhil Mehra, Rohit Sikand, Samarth Sharma, Vibhu Saujanya Kaulgud, Vikrant Podder, Sanjay Burden, Adam P. |
| author_facet | Pathania, Priyavanshi Bamby, Nikhil Mehra, Rohit Sikand, Samarth Sharma, Vibhu Saujanya Kaulgud, Vikrant Podder, Sanjay Burden, Adam P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead Pathania, Priyavanshi Bamby, Nikhil Mehra, Rohit Sikand, Samarth Sharma, Vibhu Saujanya Kaulgud, Vikrant Podder, Sanjay Burden, Adam P. Software Engineering Computers and Society 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. |
| title | Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead |
| topic | Software Engineering Computers and Society |
| url | https://arxiv.org/abs/2506.09683 |