评估AI代码生成服务的碳足迹，揭示其环境影响。
Carbon Footprint Evaluation of Code Generation through LLM as a Service
- 用GitHub Copilot生成代码，分析其碳排放
- 区分代码的嵌入式与运行时碳足迹
- 为汽车等行业的绿色编程提供依据
随着计算需求增长，数据中心能耗与碳排放日益增加，未来在大数据分析、数字化及大模型应用扩张背景下将更加显著。为降低软件开发的环境影响，绿色编程和声称AI可提升能效的观点日益流行。尤其在汽车领域，软件决定性能、安全与用户体验，绿色编程与AI驱动效率对减少行业碳足迹具有重要意义。本文综述绿色编程理念，提出衡量AI模型可持续性意识的指标体系。研究采用代码生成型商用AI语言模型GitHub Copilot作为服务范例，通过可持续性指标量化其碳足迹，定义代码的嵌入式碳（embodied carbon）与运行时碳（operational carbon），为评估生成式AI的环境影响提供方法框架。
原文摘要 · Abstract (English)
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.
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