Carbon Footprint Evaluation of Code Generation through LLM as a Service
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913771612536832 |
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| author | Vartziotis, Tina Schmidt, Maximilian Dasoulas, George Dellatolas, Ippolyti Attademo, Stefano Le, Viet Dung Wiechmann, Anke Hoffmann, Tim Keckeisen, Michael Kotsopoulos, Sotirios |
| author_facet | Vartziotis, Tina Schmidt, Maximilian Dasoulas, George Dellatolas, Ippolyti Attademo, Stefano Le, Viet Dung Wiechmann, Anke Hoffmann, Tim Keckeisen, Michael Kotsopoulos, Sotirios |
| contents | Due to increased computing use, data centers consume and emit a lot of energy and carbon. These contributions are expected to rise as big data analytics, digitization, and large AI models grow and become major components of daily working routines. To reduce the environmental impact of software development, green (sustainable) coding and claims that AI models can improve energy efficiency have grown in popularity. Furthermore, in the automotive industry, where software increasingly governs vehicle performance, safety, and user experience, the principles of green coding and AI-driven efficiency could significantly contribute to reducing the sector's environmental footprint. We present an overview of green coding and metrics to measure AI model sustainability awareness. This study introduces LLM as a service and uses a generative commercial AI language model, GitHub Copilot, to auto-generate code. Using sustainability metrics to quantify these AI models' sustainability awareness, we define the code's embodied and operational carbon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Carbon Footprint Evaluation of Code Generation through LLM as a Service Vartziotis, Tina Schmidt, Maximilian Dasoulas, George Dellatolas, Ippolyti Attademo, Stefano Le, Viet Dung Wiechmann, Anke Hoffmann, Tim Keckeisen, Michael Kotsopoulos, Sotirios Computers and Society Machine Learning Software Engineering Due to increased computing use, data centers consume and emit a lot of energy and carbon. These contributions are expected to rise as big data analytics, digitization, and large AI models grow and become major components of daily working routines. To reduce the environmental impact of software development, green (sustainable) coding and claims that AI models can improve energy efficiency have grown in popularity. Furthermore, in the automotive industry, where software increasingly governs vehicle performance, safety, and user experience, the principles of green coding and AI-driven efficiency could significantly contribute to reducing the sector's environmental footprint. We present an overview of green coding and metrics to measure AI model sustainability awareness. This study introduces LLM as a service and uses a generative commercial AI language model, GitHub Copilot, to auto-generate code. Using sustainability metrics to quantify these AI models' sustainability awareness, we define the code's embodied and operational carbon. |
| title | Carbon Footprint Evaluation of Code Generation through LLM as a Service |
| topic | Computers and Society Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2504.01036 |