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.

翻译：随着计算使用量的增加，数据中心消耗并排放大量能源与碳。随着大数据分析、数字化和大型AI模型的增长并成为日常工作流程的主要组成部分，这些能耗与碳排放预计将持续上升。为降低软件开发对环境的影响，绿色（可持续）编码以及AI模型可提升能源效率的主张日益受到关注。此外，在软件日益主导车辆性能、安全性和用户体验的汽车行业，绿色编码原则与AI驱动的效率提升可能显著助力降低该行业的环境足迹。本文概述了绿色编码及衡量AI模型可持续性意识的指标。本研究引入LLM即服务模式，并利用生成式商业AI语言模型GitHub Copilot进行代码自动生成。通过可持续性指标量化这些AI模型的可持续性意识，我们定义了代码的隐含碳与运行碳。