The increasing use of information technology has led to a significant share
of energy consumption and carbon emissions from data centers. These
contributions are expected to rise with the growing demand for big data
analytics, increasing digitization, and the development of large artificial
intelligence (AI) models. The need to address the environmental impact of
software development has led to increased interest in green (sustainable)
coding and claims that the use of AI models can lead to energy efficiency
gains. Here, we provide an empirical study on green code and an overview of
green coding practices, as well as metrics used to quantify the sustainability
awareness of AI models. In this framework, we evaluate the sustainability of
auto-generated code. The auto-generate codes considered in this study are
produced by generative commercial AI language models, GitHub Copilot, OpenAI
ChatGPT-3, and Amazon CodeWhisperer. Within our methodology, in order to
quantify the sustainability awareness of these AI models, we propose a
definition of the code's "green capacity", based on certain sustainability
metrics. We compare the performance and green capacity of human-generated code
and code generated by the three AI language models in response to easy-to-hard
problem statements. Our findings shed light on the current capacity of AI
models to contribute to sustainable software development.

信息技术的增长导致数据中心耗能和碳排放占比的显著增加。为了应对软件开发的环境影响，绿色编码和使用人工智能模型可提高能源效率已成为研究的热点。本研究以可持续性指标为基础，对自动生成代码的可持续性意识进行了评估，并比较了人工生成代码与三种人工智能语言模型生成代码在易难问题陈述下的性能和可持续性能力。结果揭示了当前人工智能模型在可持续软件开发方面的贡献能力。