Carbon Footprint Evaluation of Code Generation through LLM as a Service
It addresses environmental concerns in software development, particularly for industries like automotive where code efficiency impacts carbon emissions, though it appears incremental in applying existing sustainability concepts to AI-generated code.
This paper tackles the problem of evaluating the carbon footprint of code generation using LLM-as-a-Service, specifically GitHub Copilot, by introducing sustainability metrics to quantify embodied and operational carbon emissions.
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.